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Yoon HS, Kim MJ, Lim KH, Kim MS, Kang BJ, Rah YC, Choi J. Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients. Diagnostics (Basel) 2024; 14:2000. [PMID: 39335679 PMCID: PMC11431653 DOI: 10.3390/diagnostics14182000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/16/2024] [Accepted: 08/26/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. METHODS We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. RESULTS The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. CONCLUSIONS Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.
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Affiliation(s)
- Hee Soo Yoon
- Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea
| | - Min Jin Kim
- Department of Biostatistics, Korea University College of Medicine, Seoul 08308, Republic of Korea
- Biomedical Research Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
| | - Kang Hyeon Lim
- Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea
| | - Min Suk Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea
| | - Byung Jae Kang
- Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea
| | - Yoon Chan Rah
- Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea
| | - June Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, Korea University College of Medicine, Ansan Hospital, Ansan 15355, Republic of Korea
- Department of Biomedical Informatics, College of Medicine, Korea University, Seoul 02841, Republic of Korea
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Liu X, Guo P, Wang D, Hsieh YL, Shi S, Dai Z, Wang D, Li H, Wang W. Applications of Machine Learning in Meniere's Disease Assessment Based on Pure-Tone Audiometry. Otolaryngol Head Neck Surg 2024. [PMID: 39194410 DOI: 10.1002/ohn.956] [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: 01/22/2024] [Revised: 07/03/2024] [Accepted: 08/10/2024] [Indexed: 08/29/2024]
Abstract
OBJECTIVE To apply machine learning models based on air conduction thresholds of pure-tone audiometry for automatic diagnosis of Meniere's disease (MD) and prediction of endolymphatic hydrops (EH). STUDY DESIGN Retrospective study. SETTING Tertiary medical center. METHODS Gadolinium-enhanced magnetic resonance imaging sequences and pure-tone audiometry data were collected. Subsequently, basic and multiple analytical features were engineered based on the air conduction thresholds of pure-tone audiometry. Later, 5 classical machine learning models were trained to diagnose MD using the engineered features. The models demonstrating excellent performance were also selected to predict EH. The model's effectiveness in MD diagnosis was compared with experienced otolaryngologists. RESULTS First, the winning light gradient boosting (LGB) machine learning model trained by multiple features demonstrates a remarkable performance on the diagnosis of MD, achieving an accuracy rate of 87%, sensitivity of 83%, specificity of 90%, and a robust area under the receiver operating characteristic curve of 0.95, which compares favorably with experienced clinicians. Second, the LGB model, with an accuracy of 78% on EH prediction, outperformed the other 3 machine learning models. Finally, a feature importance analysis reveals a pivotal role of the specific pure-tone audiometry features that are essential for both MD diagnosis and EH prediction. Highlighted features include standard deviation and mean of the whole-frequency hearing, the peak of the audiogram, and hearing at low frequencies, notably at 250 Hz. CONCLUSION An efficient machine learning model based on pure-tone audiometry features was produced to diagnose MD, which also showed the potential to predict the subtypes of EH. The innovative approach demonstrated a game-changing strategy for MD screening and promising cost-effective benefits for the health care enterprise.
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Affiliation(s)
- Xu Liu
- Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China
- Department of Otorhinolaryngology, NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Ping Guo
- Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China
- Department of Otorhinolaryngology, NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Dan Wang
- Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China
- Department of Otorhinolaryngology, NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Yue-Lin Hsieh
- Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China
- Department of Otorhinolaryngology, NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Suming Shi
- Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China
- Department of Otorhinolaryngology, NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Zhijian Dai
- Department of Otorhinolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Wenzhou Medical College, Wenzhou, China
| | - Deping Wang
- Department of Otorhinolaryngology-Head and Neck Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongzhe Li
- Research Service, VA Loma Linda Healthcare System, Loma Linda, California, USA
- Department of Otolaryngology-Head and Neck Surgery, Loma Linda University School of Medicine, Loma Linda, California, USA
| | - Wuqing Wang
- Department of Otorhinolaryngology, Eye and ENT Hospital, ENT Institute, Fudan University, Shanghai, China
- Department of Otorhinolaryngology, NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
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Wu Y, Yao J, Xu XM, Zhou LL, Salvi R, Ding S, Gao X. Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study. Front Neurosci 2024; 18:1402039. [PMID: 38933814 PMCID: PMC11201293 DOI: 10.3389/fnins.2024.1402039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 05/13/2024] [Indexed: 06/28/2024] Open
Abstract
Purpose Sensorineural hearing loss (SNHL) is the most common form of sensory deprivation and is often unrecognized by patients, inducing not only auditory but also nonauditory symptoms. Data-driven classifier modeling with the combination of neural static and dynamic imaging features could be effectively used to classify SNHL individuals and healthy controls (HCs). Methods We conducted hearing evaluation, neurological scale tests and resting-state MRI on 110 SNHL patients and 106 HCs. A total of 1,267 static and dynamic imaging characteristics were extracted from MRI data, and three methods of feature selection were computed, including the Spearman rank correlation test, least absolute shrinkage and selection operator (LASSO) and t test as well as LASSO. Linear, polynomial, radial basis functional kernel (RBF) and sigmoid support vector machine (SVM) models were chosen as the classifiers with fivefold cross-validation. The receiver operating characteristic curve, area under the curve (AUC), sensitivity, specificity and accuracy were calculated for each model. Results SNHL subjects had higher hearing thresholds in each frequency, as well as worse performance in cognitive and emotional evaluations, than HCs. After comparison, the selected brain regions using LASSO based on static and dynamic features were consistent with the between-group analysis, including auditory and nonauditory areas. The subsequent AUCs of the four SVM models (linear, polynomial, RBF and sigmoid) were as follows: 0.8075, 0.7340, 0.8462 and 0.8562. The RBF and sigmoid SVM had relatively higher accuracy, sensitivity and specificity. Conclusion Our research raised attention to static and dynamic alterations underlying hearing deprivation. Machine learning-based models may provide several useful biomarkers for the classification and diagnosis of SNHL.
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Affiliation(s)
- Yuanqing Wu
- Department of Otorhinolaryngology Head and Neck Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Department of Otorhinolaryngology Head and Neck Surgery, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jun Yao
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xiao-Min Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Lei-Lei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Richard Salvi
- Center for Hearing and Deafness, University at Buffalo, The State University of New York, Buffalo, NY, United States
| | - Shaohua Ding
- Department of Radiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, China
| | - Xia Gao
- Department of Otorhinolaryngology Head and Neck Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
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Chen PY, Yang TW, Tseng YS, Tsai CY, Yeh CS, Lee YH, Lin PH, Lin TC, Wu YJ, Yang TH, Chiang YT, Hsu JSJ, Hsu CJ, Chen PL, Chou CF, Wu CC. Machine learning-based longitudinal prediction for GJB2-related sensorineural hearing loss. Comput Biol Med 2024; 176:108597. [PMID: 38763069 DOI: 10.1016/j.compbiomed.2024.108597] [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: 12/12/2023] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/21/2024]
Abstract
BACKGROUND Recessive GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for GJB2-related SNHL using machine learning to enable personalized medical planning for timely intervention. METHOD Patients with SNHL with confirmed biallelic GJB2 variants in a nationwide cohort between 2005 and 2022 were included. Different data preprocessing protocols and computational algorithms were combined to construct a prediction model. We randomly divided the dataset into training, validation, and test sets at a ratio of 72:8:20, and repeated this process ten times to obtain an average result. The performance of the models was evaluated using the mean absolute error (MAE), which refers to the discrepancy between the predicted and actual hearing thresholds. RESULTS We enrolled 449 patients with 2184 audiograms available for deep learning analysis. SNHL progression was identified in all models and was independent of age, sex, and genotype. The average hearing progression rate was 0.61 dB HL per year. The best MAE for linear regression, multilayer perceptron, long short-term memory, and attention model were 4.42, 4.38, 4.34, and 4.76 dB HL, respectively. The long short-term memory model performed best with an average MAE of 4.34 dB HL and acceptable accuracy for up to 4 years. CONCLUSIONS We have developed a prognostic model that uses machine learning to approximate realistic hearing progression in GJB2-related SNHL, allowing for the design of individualized medical plans, such as recommending the optimal follow-up interval for this population.
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Affiliation(s)
- Pey-Yu Chen
- Department of Otolaryngology, MacKay Memorial Hospital, Taipei, Taiwan; Department of Audiology and Speech-Language Pathology, Mackay Medical College, New Taipei City, Taiwan; Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan
| | - Ta-Wei Yang
- Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan
| | - Yi-Shan Tseng
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Cheng-Yu Tsai
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chiung-Szu Yeh
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yen-Hui Lee
- Department of Otolaryngology, National Taiwan University Biomedical Park Hospital, Hsinchu County, Taiwan; Department of Otolaryngology, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu City, Taiwan; Hearing and Speech Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Pei-Hsuan Lin
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ting-Chun Lin
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Yu-Jen Wu
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Ting-Hua Yang
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yu-Ting Chiang
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jacob Shu-Jui Hsu
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chuan-Jen Hsu
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Department of Otorhinolaryngology-Head and Neck Surgery, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan; School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Pei-Lung Chen
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
| | - Chen-Fu Chou
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Chen-Chi Wu
- Department of Otolaryngology, National Taiwan University Hospital, Taipei, Taiwan; Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan; Department of Medical Research, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan.
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Wang Y, Yao X, Wang D, Ye C, Xu L. A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population. BMC Public Health 2024; 24:1160. [PMID: 38664666 PMCID: PMC11044481 DOI: 10.1186/s12889-024-18636-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 04/17/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND Hearing impairment (HI) has become a major public health issue in China. Currently, due to the limitations of primary health care, the gold standard for HI diagnosis (pure-tone hearing test) is not suitable for large-scale use in community settings. Therefore, the purpose of this study was to develop a cost-effective HI screening model for the general population using machine learning (ML) methods and data gathered from community-based scenarios, aiming to help improve the hearing-related health outcomes of community residents. METHODS This study recruited 3371 community residents from 7 health centres in Zhejiang, China. Sixty-eight indicators derived from questionnaire surveys and routine haematological tests were delivered and used for modelling. Seven commonly used ML models (the naive Bayes (NB), K-nearest neighbours (KNN), support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), boosting, and least absolute shrinkage and selection operator (LASSO regression)) were adopted and compared to develop the final high-frequency hearing impairment (HFHI) screening model for community residents. The model was constructed with a nomogram to obtain the risk score of the probability of individuals suffering from HFHI. According to the risk score, the population was divided into three risk stratifications (low, medium and high) and the risk factor characteristics of each dimension under different risk stratifications were identified. RESULTS Among all the algorithms used, the LASSO-based model achieved the best performance on the validation set by attaining an area under the curve (AUC) of 0.868 (95% confidence interval (CI): 0.847-0.889) and reaching precision, specificity and F-score values all greater than 80%. Five demographic indicators, 7 disease-related features, 5 behavioural factors, 2 environmental exposures, 2 hearing cognitive factors, and 13 blood test indicators were identified in the final screening model. A total of 91.42% (1235/1129) of the subjects in the high-risk group were confirmed to have HI by audiometry, which was 3.99 times greater than that in the low-risk group (22.91%, 301/1314). The high-risk population was mainly characterized as older, low-income and low-educated males, especially those with multiple chronic conditions, noise exposure, poor lifestyle, abnormal blood indices (e.g., red cell distribution width (RDW) and platelet distribution width (PDW)) and liver function indicators (e.g., triglyceride (TG), indirect bilirubin (IBIL), aspartate aminotransferase (AST) and low-density lipoprotein (LDL)). An HFHI nomogram was further generated to improve the operability of the screening model for community applications. CONCLUSIONS The HFHI risk screening model developed based on ML algorithms can more accurately identify residents with HFHI by categorizing them into the high-risk groups, which can further help to identify modifiable and immutable risk factors for residents at high risk of HI and promote their personalized HI prevention or intervention.
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Affiliation(s)
- Yi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China
- Hangzhou Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Xinmeng Yao
- Department of Epidemiology and Biostatistics, School of Public Health, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China
| | - Dahui Wang
- Department of Health Management, School of Public Health, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Chengyin Ye
- Department of Health Management, School of Public Health, Hangzhou Normal University, Hangzhou, Zhejiang, China.
| | - Liangwen Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China.
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Omar A, Abd El-Hafeez T. Quantum computing and machine learning for Arabic language sentiment classification in social media. Sci Rep 2023; 13:17305. [PMID: 37828056 PMCID: PMC10570340 DOI: 10.1038/s41598-023-44113-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 10/03/2023] [Indexed: 10/14/2023] Open
Abstract
With the increasing amount of digital data generated by Arabic speakers, the need for effective and efficient document classification techniques is more important than ever. In recent years, both quantum computing and machine learning have shown great promise in the field of document classification. However, there is a lack of research investigating the performance of these techniques on the Arabic language. This paper presents a comparative study of quantum computing and machine learning for two datasets of Arabic language document classification. In the first dataset of 213,465 Arabic tweets, both classic machine learning (ML) and quantum computing approaches achieve high accuracy in sentiment analysis, with quantum computing slightly outperforming classic ML. Quantum computing completes the task in approximately 59 min, slightly faster than classic ML, which takes around 1 h. The precision, recall, and F1 score metrics indicate the effectiveness of both approaches in predicting sentiment in Arabic tweets. Classic ML achieves precision, recall, and F1 score values of 0.8215, 0.8175, and 0.8121, respectively, while quantum computing achieves values of 0.8239, 0.8199, and 0.8147, respectively. In the second dataset of 44,000 tweets, both classic ML (using the Random Forest algorithm) and quantum computing demonstrate significantly reduced processing times compared to the first dataset, with no substantial difference between them. Classic ML completes the analysis in approximately 2 min, while quantum computing takes approximately 1 min and 53 s. The accuracy of classic ML is higher at 0.9241 compared to 0.9205 for quantum computing. However, both approaches achieve high precision, recall, and F1 scores, indicating their effectiveness in accurately predicting sentiment in the dataset. Classic ML achieves precision, recall, and F1 score values of 0.9286, 0.9241, and 0.9249, respectively, while quantum computing achieves values of 0.92456, 0.9205, and 0.9214, respectively. The analysis of the metrics indicates that quantum computing approaches are effective in identifying positive instances and capturing relevant sentiment information in large datasets. On the other hand, traditional machine learning techniques exhibit faster processing times when dealing with smaller dataset sizes. This study provides valuable insights into the strengths and limitations of quantum computing and machine learning for Arabic document classification, emphasizing the potential of quantum computing in achieving high accuracy, particularly in scenarios where traditional machine learning techniques may encounter difficulties. These findings contribute to the development of more accurate and efficient document classification systems for Arabic data.
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Affiliation(s)
- Ahmed Omar
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
| | - Tarek Abd El-Hafeez
- Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt.
- Computer Science Unit, Deraya University, EL-Minia, Egypt.
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Ensemble filters with harmonize PSO-SVM algorithm for optimal hearing disorder prediction. Neural Comput Appl 2023; 35:10473-10496. [PMID: 36747886 PMCID: PMC9894525 DOI: 10.1007/s00521-023-08244-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 01/06/2023] [Indexed: 02/05/2023]
Abstract
Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction.
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8
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Alsoof D, McDonald CL, Kuris EO, Daniels AH. Machine Learning for the Orthopaedic Surgeon: Uses and Limitations. J Bone Joint Surg Am 2022; 104:1586-1594. [PMID: 35383655 DOI: 10.2106/jbjs.21.01305] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
➤ Machine learning is a subset of artificial intelligence in which computer algorithms are trained to make classifications and predictions based on patterns in data. The utilization of these techniques is rapidly expanding in the field of orthopaedic research. ➤ There are several domains in which machine learning has application to orthopaedics, including radiographic diagnosis, gait analysis, implant identification, and patient outcome prediction. ➤ Several limitations prevent the widespread use of machine learning in the daily clinical environment. However, future work can overcome these issues and enable machine learning tools to be a useful adjunct for orthopaedic surgeons in their clinical decision-making.
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Affiliation(s)
- Daniel Alsoof
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Providence, Rhode Island
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Zeng J, Kang W, Chen S, Lin Y, Deng W, Wang Y, Chen G, Ma K, Zhao F, Zheng Y, Liang M, Zeng L, Ye W, Li P, Chen Y, Chen G, Gao J, Wu M, Su Y, Zheng Y, Cai Y. A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images. JAMA Otolaryngol Head Neck Surg 2022; 148:612-620. [PMID: 35588049 DOI: 10.1001/jamaoto.2022.0900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients. Objective To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features. Design, Setting, and Participants A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features. Main Outcomes and Measures The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, which is the harmonic mean of precision and recall; a higher F1 score means better performance). In addition, these evaluation parameters were compared to results obtained from the logistic regression model and predictions made by three otologists. Results The performance of the DL model in predicting CHL showed the AUC of 0.74, accuracy of 81%, and F1 score of 0.89. This was better than the results from the logistic regression model (ie, AUC of 0.60, accuracy of 76%, and F1 score of 0.82), and much improved on the performance of the 3 otologists; accuracy of 16%, 30%, 39%, and F1 scores of 0.09, 0.18, and 0.25, respectively. Furthermore, the DL model took 2.5 seconds to predict from 205 otoscopic images, whereas the 3 otologists spent 633 seconds, 645 seconds, and 692 seconds, respectively. Conclusions and Relevance The model in this diagnostic/prognostic study provided greater accuracy in prediction of CHL in ears with OME than those obtained from the logistic regression model and otologists. This indicates great potential for the use of artificial intelligence tools to facilitate CHL evaluation when CHL is unable to be measured.
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Affiliation(s)
- Junbo Zeng
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weibiao Kang
- The second Hospital, Medical College, Shantou University, Shantou, Guangdong Province, China
| | - Suijun Chen
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi Lin
- Jarvis Lab, Tencent, Shen Zhen city, Guangdong Province, China.,Hong Kong University of Science and Technology, Hong Kong, China
| | - Wenting Deng
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yajing Wang
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Guisheng Chen
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kai Ma
- Jarvis Lab, Tencent, Shen Zhen city, Guangdong Province, China
| | - Fei Zhao
- Centre for Speech and Language Therapy and Hearing Science, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Wales, United Kingdom
| | - Yefeng Zheng
- Jarvis Lab, Tencent, Shen Zhen city, Guangdong Province, China
| | - Maojin Liang
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Linqi Zeng
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Weijie Ye
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Peng Li
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yubin Chen
- Department of Otolaryngology-Head and Neck Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Guoping Chen
- Department of Otolaryngology, Zhongshan City People's Hospital, Zhongshan Affiliated Hospital of Sun Yat-sen University, Zhongshan, Guangdong Province, China
| | - Jinliang Gao
- Department of Otolaryngology, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, Guangdong Province, China
| | - Minjian Wu
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuejia Su
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yiqing Zheng
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Shenzhen-Shanwei Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei City, Guangdong Province, China
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.,Shenzhen-Shanwei Central Hospital, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei City, Guangdong Province, China
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Wang S, Celebi ME, Zhang YD, Yu X, Lu S, Yao X, Zhou Q, Miguel MG, Tian Y, Gorriz JM, Tyukin I. Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects. INFORMATION FUSION 2021; 76:376-421. [DOI: 10.1016/j.inffus.2021.07.001] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/30/2023]
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