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Zhao L, Yin J, Huan J, Han X, Zhao D, Song J, Wang L, Zhang H, Pan B, Niu Q, Lu X. A Bayesian network for estimating hypertension risk due to occupational aluminum exposure. Chronic Dis Transl Med 2024; 10:130-139. [PMID: 38872757 PMCID: PMC11166680 DOI: 10.1002/cdt3.134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/07/2024] [Accepted: 05/10/2024] [Indexed: 06/15/2024] Open
Abstract
Background The correlation between metals and hypertension, such as sodium, zinc, potassium, and magnesium, has been confirmed, while the relationship between aluminum and hypertension is not very clear. This study aimed to evaluate the correlation between plasma aluminum and hypertension in electrolytic aluminum workers by the Bayesian networks (BN). Methods In 2019, 476 male workers in an aluminum factory were investigated. The plasma aluminum concentration of workers was measured by inductively coupled plasma mass spectrometry. The influencing factors on the prevalence of hypertension were analyzed by the BN. Results The prevalence of hypertension was 23.9% in 476 male workers. The risk of hypertension from plasma aluminum in the Q2, Q3, and Q4 groups was 5.20 (1.90-14.25), 6.92 (2.51-19.08), and 7.33 (2.69-20.01), respectively, compared with that in the Q1 group. The risk of hypertension from the duration of exposure to aluminum of >10 years was 2.23 (1.09-4.57), compared without aluminum exposure. Area under the curve was 0.80 of plasma aluminum and the duration of exposure to aluminum was based on covariates, indicating that aluminum exposure had important predictive value in the prevalence of hypertension in the occupational population. The results of the study using the BN model showed that if the plasma aluminum of all participants was higher than Q4 (≥47.86 µg/L) and the participants were drinking, smoking, diabetes, central obesity, dyslipidemia, and aged >50 years, the proportion of hypertension was 71.2%. Conclusions The prevalence of hypertension increased significantly with the increase of plasma aluminum level.
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Affiliation(s)
- Le Zhao
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Jinzhu Yin
- Sinopharm Tongmei General HospitalShanxi Health Commission Key Laboratory of Nervous System Disease Prevention and TreatmentDatongShanxiChina
| | - Jiaping Huan
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Xiao Han
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Dan Zhao
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Jing Song
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Linping Wang
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Huifang Zhang
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Baolong Pan
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
- Sixth Hospital of Shanxi Medical University (General Hospital of Tisco)TaiyuanShanxiChina
| | - Qiao Niu
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
| | - Xiaoting Lu
- Department of Occupational Health, School of Public Health, Shanxi Key Laboratory of Environmental Health Impairment and Prevention, MOE Key Laboratory of Coal Environmental Pathogenicity and PreventionShanxi Medical UniversityTaiyuanShanxiChina
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Leonhard C. Review of Statistical and Methodological Issues in the Forensic Prediction of Malingering from Validity Tests: Part I: Statistical Issues. Neuropsychol Rev 2023; 33:581-603. [PMID: 37612531 DOI: 10.1007/s11065-023-09601-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 03/29/2023] [Indexed: 08/25/2023]
Abstract
Forensic neuropsychological examinations with determination of malingering have tremendous social, legal, and economic consequences. Thousands of studies have been published aimed at developing and validating methods to diagnose malingering in forensic settings, based largely on approximately 50 validity tests, including embedded and stand-alone performance validity tests. This is the first part of a two-part review. Part I explores three statistical issues related to the validation of validity tests as predictors of malingering, including (a) the need to report a complete set of classification accuracy statistics, (b) how to detect and handle collinearity among validity tests, and (c) how to assess the classification accuracy of algorithms for aggregating information from multiple validity tests. In the Part II companion paper, three closely related research methodological issues will be examined. Statistical issues are explored through conceptual analysis, statistical simulations, and through reanalysis of findings from prior validation studies. Findings suggest extant neuropsychological validity tests are collinear and contribute redundant information to the prediction of malingering among forensic examinees. Findings further suggest that existing diagnostic algorithms may miss diagnostic accuracy targets under most realistic conditions. The review makes several recommendations to address these concerns, including (a) reporting of full confusion table statistics with 95% confidence intervals in diagnostic trials, (b) the use of logistic regression, and (c) adoption of the consensus model on the "transparent reporting of multivariate prediction models for individual prognosis or diagnosis" (TRIPOD) in the malingering literature.
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Affiliation(s)
- Christoph Leonhard
- The Chicago School of Professional Psychology at Xavier University of Louisiana, Box 200, 1 Drexel Dr, New Orleans, LA, 70125, USA.
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A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5905230. [PMID: 36569180 PMCID: PMC9788902 DOI: 10.1155/2022/5905230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/17/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.
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Fan ZX, Wang CB, Fang LB, Ma L, Niu TT, Wang ZY, Lu JF, Yuan BY, Liu GZ. Risk factors and a Bayesian network model to predict ischemic stroke in patients with dilated cardiomyopathy. Front Neurosci 2022; 16:1043922. [PMID: 36440270 PMCID: PMC9683474 DOI: 10.3389/fnins.2022.1043922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/25/2022] [Indexed: 04/03/2024] Open
Abstract
OBJECTIVE This study aimed to identify risk factors and create a predictive model for ischemic stroke (IS) in patients with dilated cardiomyopathy (DCM) using the Bayesian network (BN) approach. MATERIALS AND METHODS We collected clinical data of 634 patients with DCM treated at three referral management centers in Beijing between 2016 and 2021, including 127 with and 507 without IS. The patients were randomly divided into training (441 cases) and test (193 cases) sets at a ratio of 7:3. A BN model was established using the Tabu search algorithm with the training set data and verified with the test set data. The BN and logistic regression models were compared using the area under the receiver operating characteristic curve (AUC). RESULTS Multivariate logistic regression analysis showed that hypertension, hyperlipidemia, atrial fibrillation/flutter, estimated glomerular filtration rate (eGFR), and intracardiac thrombosis were associated with IS. The BN model found that hyperlipidemia, atrial fibrillation (AF) or atrial flutter, eGFR, and intracardiac thrombosis were closely associated with IS. Compared to the logistic regression model, the BN model for IS performed better or equally well in the training and test sets, with respective accuracies of 83.7 and 85.5%, AUC of 0.763 [95% confidence interval (CI), 0.708-0.818] and 0.822 (95% CI, 0.748-0.896), sensitivities of 20.2 and 44.2%, and specificities of 98.3 and 97.3%. CONCLUSION Hypertension, hyperlipidemia, AF or atrial flutter, low eGFR, and intracardiac thrombosis were good predictors of IS in patients with DCM. The BN model was superior to the traditional logistic regression model in predicting IS in patients with DCM and is, therefore, more suitable for early IS detection and diagnosis, and could help prevent the occurrence and recurrence of IS in this patient cohort.
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Affiliation(s)
- Ze-Xin Fan
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Chao-Bin Wang
- Department of Neurology, Beijing Fangshan District Liangxiang Hospital, Beijing, China
| | - Li-Bo Fang
- Department of Neurology, Beijing Fuxing Hospital, Capital Medical University, Beijing, China
| | - Lin Ma
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Tian-Tong Niu
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Ze-Yi Wang
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Jian-Feng Lu
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Bo-Yi Yuan
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Guang-Zhi Liu
- Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
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Quan D, Ren J, Ren H, Linghu L, Wang X, Li M, Qiao Y, Ren Z, Qiu L. Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network. Sci Rep 2022; 12:7563. [PMID: 35534641 PMCID: PMC9085890 DOI: 10.1038/s41598-022-11125-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 04/08/2022] [Indexed: 01/15/2023] Open
Abstract
AbstractThis study aimed to construct Bayesian networks (BNs) to analyze the network relationships between COPD and its influencing factors, and the strength of each factor's influence on COPD was reflected through network reasoning. Elastic Net and Max-Min Hill-Climbing (MMHC) algorithm were adopted to screen the variables on the surveillance data of COPD among residents in Shanxi Province, China from 2014 to 2015, and construct BNs respectively. 10 variables finally entered the model after screening by Elastic Net. The BNs constructed by MMHC showed that smoking status, household air pollution, family history, cough, air hunger or dyspnea were directly related to COPD, and Gender was indirectly linked to COPD through smoking status. Moreover, smoking status, household air pollution and family history were the parent nodes of COPD, and cough, air hunger or dyspnea represented the child nodes of COPD. In other words, smoking status, household air pollution and family history were related to the occurrence of COPD, and COPD would make patients’ cough, air hunger or dyspnea worse. Generally speaking, BNs could reveal the complex network linkages between COPD and its relevant factors well, making it more convenient to carry out targeted prevention and control of COPD.
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Makond B, Wang KJ, Wang KM. Benchmarking prognosis methods for survivability - A case study for patients with contingent primary cancers. Comput Biol Med 2021; 138:104888. [PMID: 34610552 DOI: 10.1016/j.compbiomed.2021.104888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 09/17/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND There is an increasing number of patients with a first primary cancer who are diagnosed with a second primary cancer, but prognosis methods to predict the survivability of a patient with multiple primary cancers have not been fully benchmarked. METHODS This study investigated the five-year survivability prognosis performances of six machine learning approaches. These approaches are: artificial neural network, decision tree (DT), logistic regression, support vector machine, naïve Bayes (NB), and Bayesian network (BN). A synthetic minority over-sampling technique (SMOTE) was used to solve the imbalanced problem, and a nationwide cancer patient database containing 7,845 subjects in Taiwan was used as a sample source. Ten primary and secondary cancers and their key variables affecting the survivability of the patients were identified. RESULTS All the models using SMOTE improved sensitivity and specificity significantly. NB has the highest performance in terms of accuracy and specificity, whereas BN has the highest performance in terms of sensitivity. Further, the computational time and the power of knowledge representation of NB, BN, and DT outperformed the others. CONCLUSIONS Selecting the appropriate prognosis models to predict survivability of patients with two contingent primary cancers can aid precise prediction and can support appropriate treatment advice.
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Affiliation(s)
- Bunjira Makond
- Faculty of Commerce and Management, Prince of Songkla University, Trang, Thailand.
| | - Kung-Jeng Wang
- Department of Industrial Management National Taiwan University of Science and Technology, Taipei, 106, ROC, Taiwan.
| | - Kung-Min Wang
- Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, R.O.C, Taiwan.
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Wang X, Pan J, Ren Z, Zhai M, Zhang Z, Ren H, Song W, He Y, Li C, Yang X, Li M, Quan D, Chen L, Qiu L. Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study. BMC Public Health 2021; 21:1375. [PMID: 34247609 PMCID: PMC8273956 DOI: 10.1186/s12889-021-11412-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 06/29/2021] [Indexed: 12/27/2022] Open
Abstract
Background This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). Methods Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. Results The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. Conclusions The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-021-11412-5.
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Affiliation(s)
- Xuchun Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Jinhua Pan
- Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
| | - Zeping Ren
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012, Shanxi, China
| | - Mengmeng Zhai
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Zhuang Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Hao Ren
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Weimei Song
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Yuling He
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012, Shanxi, China
| | - Chenglian Li
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012, Shanxi, China
| | - Xiaojuan Yang
- Shanxi Centre for Disease Control and Prevention, Taiyuan, 030012, Shanxi, China
| | - Meichen Li
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Dichen Quan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Limin Chen
- Shanxi Provincial People's Hospital, Taiyuan city, Shanxi Province, China.
| | - Lixia Qiu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
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Taqvi SAA, Zabiri H, Tufa LD, Uddin F, Fatima SA, Maulud AS. A Review on Data‐Driven Learning Approaches for Fault Detection and Diagnosis in Chemical Processes. CHEMBIOENG REVIEWS 2021. [DOI: 10.1002/cben.202000027] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Syed Ali Ammar Taqvi
- NED University of Engineering & Technology Department of Chemical Engineering 75270 Karachi Pakistan
- NED University of Engineering and Technology Neurocomputation Lab, National Centre of Artificial Intelligence 75270 Karachi Pakistan
| | - Haslinda Zabiri
- Universiti Teknologi PETRONAS Chemical Engineering Department 32610 Seri Iskandar, Perak Darul Ridzuan Malaysia
| | - Lemma Dendena Tufa
- Addis Ababa Institute of Technology School of Chemical and Bioengineering King George VI St 1000 Addis Ababa Ethiopia
| | - Fahim Uddin
- NED University of Engineering & Technology Department of Chemical Engineering 75270 Karachi Pakistan
| | - Syeda Anmol Fatima
- Universiti Teknologi PETRONAS Chemical Engineering Department 32610 Seri Iskandar, Perak Darul Ridzuan Malaysia
| | - Abdulhalim Shah Maulud
- Universiti Teknologi PETRONAS Chemical Engineering Department 32610 Seri Iskandar, Perak Darul Ridzuan Malaysia
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Wang KM, Wang KJ, Makond B. Survivability modelling using Bayesian network for patients with first and secondary primary cancers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 196:105686. [PMID: 32777652 DOI: 10.1016/j.cmpb.2020.105686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 07/29/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables. METHODS In this study, a Bayesian network (BN) model was proposed to describe the occurrence of two primary cancers and predict the five-year survivability of patients using probabilistic evidence. Eleven types of major primary cancers and contingent occurrences of secondary cancers were investigated. A nationwide two-cancer database involving 7,845 patients in Taiwan was investigated. The BN topology is rigorously examined and imbalanced dataset is processed by the synthetic minority oversampling technique. The proposed BN survivability prognosis model was compared with benchmark approaches. RESULTS The proposed model significantly outperformed the back-propagation neural network, logistic regression, support vector machine, and naïve Bayes in terms of sensitivity, which is a critical performance index for the non-survival group. CONCLUSIONS Using the proposed BN model, one can estimate the posterior probabilities for every query provided appropriate prior evidences. The potential survivability information of patients, treatment effects, and socio-demographics factor effects predicted by the proposed model can help in cancer treatment assessment and cancer development monitoring.
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Affiliation(s)
- Kung-Min Wang
- Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, ROC.
| | - Kung-Jeng Wang
- Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, 106, Taiwan, ROC.
| | - Bunjira Makond
- Faculty of Commerce and Management, Prince of Songkla University, Trang, Thailand
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Benmouna Y, Mezmaz MS, Mahmoudi S, Chikh MA. Parallel cycle-based branch-and-bound method for Bayesian network learning. Pattern Anal Appl 2020. [DOI: 10.1007/s10044-019-00815-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Wang KJ, Chen JL, Chen KH, Wang KM. Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling. J Med Syst 2020; 44:65. [PMID: 32040648 DOI: 10.1007/s10916-020-1537-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 02/07/2020] [Indexed: 11/30/2022]
Abstract
Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.
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Affiliation(s)
- Kung-Jeng Wang
- Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei, 106, Taiwan, People's Republic of China.
| | - Jyun-Lin Chen
- Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei, 106, Taiwan, People's Republic of China
| | - Kun-Huang Chen
- CTBC Financial Management College, No. 600, Sec. 3, Taijiang Blvd., Annan District, Tainan City, 709, Taiwan, People's Republic of China
| | - Kung-Min Wang
- Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Shilin District, Taipei, 111, Taiwan, People's Republic of China
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Sheng B, Huang L, Wang X, Zhuang J, Tang L, Deng C, Zhang Y. Identification of Knee Osteoarthritis Based on Bayesian Network: Pilot Study. JMIR Med Inform 2019; 7:e13562. [PMID: 31322132 PMCID: PMC6670282 DOI: 10.2196/13562] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 05/21/2019] [Accepted: 05/31/2019] [Indexed: 01/13/2023] Open
Abstract
Background Early identification of knee osteoarthritis (OA) can improve treatment outcomes and reduce medical costs. However, there are major limitations among existing classification or prediction models, including abstract data processing and complicated dataset attributes, which hinder their applications in clinical practice. Objective The aim of this study was to propose a Bayesian network (BN)–based classification model to classify people with knee OA. The proposed model can be treated as a prescreening tool, which can provide decision support for health professionals. Methods The proposed model’s structure was based on a 3-level BN structure and then retrained by the Bayesian Search (BS) learning algorithm. The model’s parameters were determined by the expectation-maximization algorithm. The used dataset included backgrounds, the target disease, and predictors. The performance of the model was evaluated based on classification accuracy, area under the curve (AUC), specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV); it was also compared with other well-known classification models. A test was also performed to explore whether physical fitness tests could improve the performance of the proposed model. Results A total of 249 elderly people between the ages of 60 and 80 years, living in the Kongjiang community (Shanghai), were recruited from April to September 2007. A total of 157 instances were adopted as the dataset after data preprocessing. The experimental results showed that the results of the proposed model were higher than, or equal to, the mean scores of other classification models: .754 for accuracy, .78 for AUC, .78 for specificity, and .73 for sensitivity. The proposed model provided .45 for PPV and .92 for NPV at the prevalence of 20%. The proposed model also showed a significant improvement when compared with the traditional BN model: 6.3% increase in accuracy (from .709 to .754), 4.0% increase in AUC (from .75 to .78), 6.8% increase in specificity (from .73 to .78), 5.8% increase in sensitivity (from .69 to .73), 15.4% increase in PPV (from .39 to .45), and 2.2% increase in NPV (from .90 to .92). Furthermore, the test results showed that the performance of the proposed model could be largely enhanced through physical fitness tests in 3 evaluation indices: 10.6% increase in accuracy (from .682 to .754), 16.4% increase in AUC (from .67 to .78), and 30.0% increase in specificity (from .60 to .78). Conclusions The proposed model presents a promising method to classify people with knee OA when compared with other classification models and the traditional BN model. It could be implemented in clinical practice as a prescreening tool for knee OA, which would not only improve the quality of health care for elderly people but also reduce overall medical costs.
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Affiliation(s)
- Bo Sheng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fujian, China.,Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand
| | - Liang Huang
- Department of Exercise Sciences, The University of Auckland, Auckland, New Zealand
| | - Xiangbin Wang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fujian, China
| | - Jie Zhuang
- School of Kinesiology, Shanghai University of Sport, Shanghai, China
| | - Lihua Tang
- Department of Mechanical Engineering, The University of Auckland, Auckland, New Zealand
| | - Chao Deng
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Yanxin Zhang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fujian, China.,Department of Exercise Sciences, The University of Auckland, Auckland, New Zealand.,School of Kinesiology, Shanghai University of Sport, Shanghai, China
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Application of tabu search-based Bayesian networks in exploring related factors of liver cirrhosis complicated with hepatic encephalopathy and disease identification. Sci Rep 2019; 9:6251. [PMID: 31000773 PMCID: PMC6472503 DOI: 10.1038/s41598-019-42791-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Accepted: 04/08/2019] [Indexed: 02/06/2023] Open
Abstract
This study aimed to explore the related factors and strengths of hepatic cirrhosis complicated with hepatic encephalopathy (HE) by multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs), and to deduce the probability of HE in patients with cirrhosis under different conditions through BN reasoning. Multivariate logistic regression analysis indicated that electrolyte disorders, infections, poor spirits, hepatorenal syndrome, hepatic diabetes, prothrombin time, and total bilirubin are associated with HE. Inferences by BNs found that infection, electrolyte disorder and hepatorenal syndrome are closely related to HE. Those three variables are also related to each other, indicating that the occurrence of any of those three complications may induce the other two complications. When those three complications occur simultaneously, the probability of HE may reach 0.90 or more. The BN constructed by the tabu search algorithm can analyze not only how the correlative factors affect HE but also their interrelationships. Reasoning using BNs can describe how HE is induced on the basis of the order in which doctors acquire patient information, which is consistent with the sequential process of clinical diagnosis and treatment.
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Wang KJ, Chen JL, Wang KM. Medical expenditure estimation by Bayesian network for lung cancer patients at different severity stages. Comput Biol Med 2019; 106:97-105. [PMID: 30708222 DOI: 10.1016/j.compbiomed.2019.01.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 01/07/2019] [Accepted: 01/19/2019] [Indexed: 11/19/2022]
Abstract
Lung cancer is one of the leading causes of mortality, and its medical expenditure has increased dramatically. Estimating the expenditure for this disease has become an urgent concern of the supporting families, medial institutes, and government. In this study, a conditional Gaussian Bayesian network (CGBN) model was developed to incorporate the comprehensive risk factors to estimate the medical expenditure of a lung cancer patient at different stages. A total of 961 patients were surveyed by the four severity stages of lung cancer. The proposed CGBN model identified the correlation and association of 15 risk factors to the medical expenditure of different severity stages of lung cancer patients. The relationships among the demographic, diagnosed-based, and prior-utilization variables are constructed. The model predicted the lung cancer-related medical expenditure with high accuracy of 32.63%, 50.30%, 50.36%, and 66.58%, respectively for stages 1-4, as compared with the reported models. A greedy search was also applied to find the upper threshold of R2, while our model also shows that it approached the upper threshold.
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Affiliation(s)
- Kung-Jeng Wang
- Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei, 106, Taiwan, ROC.
| | - Jyun-Lin Chen
- Department of Industrial Management, National Taiwan University of Science and Technology, No.43, Sec. 4, Keelung Rd., Da'an Dist., Taipei, 106, Taiwan, ROC.
| | - Kung-Min Wang
- Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Shilin District, Taipei, 111, Taiwan, ROC.
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Prevalence of hyperlipidemia in Shanxi Province, China and application of Bayesian networks to analyse its related factors. Sci Rep 2018; 8:3750. [PMID: 29491353 PMCID: PMC5830606 DOI: 10.1038/s41598-018-22167-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 02/19/2018] [Indexed: 12/11/2022] Open
Abstract
This study aimed to obtain the prevalence of hyperlipidemia and its related factors in Shanxi Province, China using multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs). A multi-stage stratified random sampling method was adopted to obtain samples among the general population aged 18 years or above. The prevalence of hyperlipidemia in Shanxi Province was 42.6%. Multivariate logistic regression analysis indicated that gender, age, region, occupation, vegetable intake level, physical activity, body mass index, central obesity, hypertension, and diabetes mellitus are associated with hyperlipidemia. BNs were used to find connections between those related factors and hyperlipidemia, which were established by a complex network structure. The results showed that BNs can not only be used to find out the correlative factors of hyperlipidemia but also to analyse how these factors affect hyperlipidemia and their interrelationships, which is consistent with practical theory, is superior to logistic regression and has better application prospects.
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Toyinbo PA, Vanderploeg RD, Belanger HG, Spehar AM, Lapcevic WA, Scott SG. A Systems Science Approach to Understanding Polytrauma and Blast-Related Injury: Bayesian Network Model of Data From a Survey of the Florida National Guard. Am J Epidemiol 2017; 185:135-146. [PMID: 27986702 DOI: 10.1093/aje/kww074] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2015] [Revised: 04/28/2016] [Accepted: 05/25/2016] [Indexed: 01/08/2023] Open
Abstract
We sought to further define the epidemiology of the complex, multiple injuries collectively known as polytrauma/blast-related injury (PT/BRI). Using a systems science approach, we performed Bayesian network modeling to find the most accurate representation of the complex system of PT/BRI and identify key variables for understanding the subsequent effects of blast exposure in a sample of Florida National Guard members (1,443 deployed to Operation Enduring Freedom/Operation Iraqi Freedom and 1,655 not deployed) who completed an online survey during the period from 2009 to 2010. We found that postdeployment symptoms reported as present at the time of the survey were largely independent of deployment per se. Blast exposure, not mild traumatic brain injury (TBI), acted as the primary military deployment-related driver of PT/BRI symptoms. Blast exposure was indirectly linked to mild TBI via other deployment-related traumas and was a significant risk for a high level of posttraumatic stress disorder (PTSD) arousal symptoms. PTSD arousal symptoms and tinnitus were directly dependent upon blast exposure, with both acting as bridge symptoms to other postdeployment mental health and physical symptoms, respectively. Neurobehavioral or postconcussion-like symptoms had no significant dependence relationship with mild TBI, but they were synergistic with blast exposure in influencing PTSD arousal symptoms. A replication of this analysis using a larger PT/BRI database is warranted.
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Langarizadeh M, Moghbeli F. Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review. Acta Inform Med 2016; 24:364-369. [PMID: 28077895 PMCID: PMC5203736 DOI: 10.5455/aim.2016.24.364-369] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2016] [Accepted: 10/11/2016] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction. OBJECTIVE This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms. METHODS PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study. RESULT In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms. DISCUSSION This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy. CONCLUSION The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.
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Affiliation(s)
- Mostafa Langarizadeh
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Fateme Moghbeli
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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Dubey AK, Gupta U, Jain S. Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis. CHINESE JOURNAL OF CANCER 2016; 35:71. [PMID: 27473753 PMCID: PMC4967338 DOI: 10.1186/s40880-016-0135-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 07/21/2016] [Indexed: 02/06/2023]
Abstract
Background Owing to the use of tobacco and the consumption of alcohol and adulterated food, worldwide cancer incidence is increasing at an alarming and frightening rate. Since the last decade of the twentieth century, lung cancer has been the most common cancer type. This study aimed to determine the global status of lung cancer and to evaluate the use of computational methods in the early detection of lung cancer. Methods We used lung cancer data from the United Kingdom (UK), the United States (US), India, and Egypt. For statistical analysis, we used incidence and mortality as well as survival rates to better understand the critical state of lung cancer. Results In the UK and the US, we found a significant decrease in lung cancer mortalities in the period of 1990–2014, whereas, in India and Egypt, such a decrease was not much promising. Additionally, we observed that, in the UK and the US, the survival rates of women with lung cancer were higher than those of men. We observed that the data mining and evolutionary algorithms were efficient in lung cancer detection. Conclusions Our findings provide an inclusive understanding of the incidences, mortalities, and survival rates of lung cancer in the UK, the US, India, and Egypt. The combined use of data mining and evolutionary algorithm can be efficient in lung cancer detection.
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Affiliation(s)
- Ashutosh Kumar Dubey
- Institute of Engineering and Technology, JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura, Ajmer Road, Jaipur, Rajasthan, 302 026, India.
| | - Umesh Gupta
- Institute of Engineering and Technology, JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura, Ajmer Road, Jaipur, Rajasthan, 302 026, India
| | - Sonal Jain
- Institute of Engineering and Technology, JK Lakshmipat University, Near Mahindra SEZ, P.O. Mahapura, Ajmer Road, Jaipur, Rajasthan, 302 026, India
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A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease. Sci Rep 2016; 6:26712. [PMID: 27273250 PMCID: PMC4896009 DOI: 10.1038/srep26712] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2016] [Accepted: 05/04/2016] [Indexed: 01/18/2023] Open
Abstract
Mild cognitive impairment (MCI) is a precursor phase of Alzheimer’s disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.
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Lau CL, Smith CS. Bayesian networks in infectious disease eco-epidemiology. REVIEWS ON ENVIRONMENTAL HEALTH 2016; 31:173-177. [PMID: 26812850 DOI: 10.1515/reveh-2015-0052] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 10/16/2015] [Indexed: 06/05/2023]
Abstract
Globally, infectious diseases are responsible for a significant burden on human health. Drivers of disease transmission depend on interactions between humans, the environment, vectors, carriers, and pathogens; transmission dynamics are therefore potentially highly complex. Research in infectious disease eco-epidemiology has been rapidly gaining momentum because of the rising global importance of disease emergence and outbreaks, and growing understanding of the intimate links between human health and the environment. The scientific community is increasingly recognising the need for multidisciplinary translational research, integrated approaches, and innovative methods and tools to optimise risk prediction and control measures. Environmental health experts have also identified the need for more advanced analytical and biostatistical approaches to better determine causality, and deal with unknowns and uncertainties inherent in complex systems. In this paper, we discuss the use of Bayesian networks in infectious disease eco-epidemiology, and the potential for developing dynamic tools for public health decision-making and improving intervention strategies.
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Makond B, Wang KJ, Wang KM. Probabilistic modeling of short survivability in patients with brain metastasis from lung cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 119:142-162. [PMID: 25804445 DOI: 10.1016/j.cmpb.2015.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2014] [Revised: 02/07/2015] [Accepted: 02/10/2015] [Indexed: 06/04/2023]
Abstract
The prediction of substantially short survivability in patients is extremely risky. In this study, we proposed a probabilistic model using Bayesian network (BN) to predict the short survivability of patients with brain metastasis from lung cancer. A nationwide cancer patient database from 1996 to 2010 in Taiwan was used. The cohort consisted of 438 patients with brain metastasis from lung cancer. We utilized synthetic minority over-sampling technique (SMOTE) to solve the imbalanced property embedded in the problem. The proposed BN was compared with three competitive models, namely, naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). Statistical analysis showed that performances of BN, LR, NB, and SVM were statistically the same in terms of all indices with low sensitivity when these models were applied on an imbalanced data set. Results also showed that SMOTE can improve the performance of the four models in terms of sensitivity, while keeping high accuracy and specificity. Further, the proposed BN is more effective as compared with NB, LR, and SVM from two perspectives: the transparency and ability to show the relation of factors affecting brain metastasis from lung cancer; it allows decision makers to find the probability despite incomplete evidence and information; and the sensitivity of the proposed BN is the highest among all standard machine learning methods.
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Affiliation(s)
- Bunjira Makond
- Faculty of Commerce and Management, Prince of Songkla University, Trang, Thailand.
| | - Kung-Jeng Wang
- Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC.
| | - Kung-Min Wang
- Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, ROC.
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