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Chen Q, Hu H, She Y, He Q, Huang X, Shi H, Cao X, Zhang X, Xu Y. An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus. Sci Rep 2024; 14:2197. [PMID: 38273015 PMCID: PMC10810925 DOI: 10.1038/s41598-024-52550-1] [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: 08/05/2023] [Accepted: 01/19/2024] [Indexed: 01/27/2024] Open
Abstract
Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.
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Affiliation(s)
- Qingquan Chen
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Haiping Hu
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Yuanyu She
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qing He
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xinfeng Huang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Huanhuan Shi
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiangyu Cao
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Xiaoyang Zhang
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China.
- School of Public Health, Fujian Medical University, Fuzhou, China.
| | - Youqiong Xu
- The Affiliated Fuzhou Center for Disease Control and Prevention of Fujian Medical University, Fuzhou, China.
- School of Public Health, Fujian Medical University, Fuzhou, China.
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Sampa MB, Biswas T, Rahman MS, Aziz NHBA, Hossain MN, Aziz NAA. A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study. JMIR Diabetes 2023; 8:e49113. [PMID: 37999944 DOI: 10.2196/49113] [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: 05/18/2023] [Revised: 09/28/2023] [Accepted: 10/11/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information. OBJECTIVE To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh. METHODS Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values. RESULTS Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly. CONCLUSIONS This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.
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Affiliation(s)
- Masuda Begum Sampa
- Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
- Department of Computer Science and Engineering, Faculty of Science, Engineering and Technology, University of Science and Technology Chittagong, Chattogram, Bangladesh
| | - Topu Biswas
- Department of Computer Science and Engineering, Faculty of Science, Engineering and Technology, University of Science and Technology Chittagong, Chattogram, Bangladesh
| | - Md Siddikur Rahman
- Department of Statistics, Faculty of Science, Begum Rokeya University, Rangpur, Bangladesh
| | - Nor Hidayati Binti Abdul Aziz
- Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
| | - Md Nazmul Hossain
- Department of Marketing, Faculty of Business Studies, University of Dhaka, Dhaka, Bangladesh
| | - Nor Azlina Ab Aziz
- Center for Engineering Computational Intelligence, Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
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Usman AG, Tanimu A, Abba SI, Isik S, Aitani A, Alasiri H. Feasibility of the Optimal Design of AI-Based Models Integrated with Ensemble Machine Learning Paradigms for Modeling the Yields of Light Olefins in Crude-to-Chemical Conversions. ACS OMEGA 2023; 8:40517-40531. [PMID: 37929092 PMCID: PMC10620777 DOI: 10.1021/acsomega.3c05227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
The prediction of the yields of light olefins in the direct conversion of crude oil to chemicals requires the development of a robust model that represents the crude-to-chemical conversion processes. This study utilizes artificial intelligence (AI) and machine learning algorithms to develop single and ensemble learning models that predict the yields of ethylene and propylene. Four single-model AI techniques and four ensemble paradigms were developed using experimental data derived from the catalytic cracking experiments of various crude oil fractions in the advanced catalyst evaluation reactor unit. The temperature, feed type, feed conversion, total gas, dry gas, and coke were used as independent variables. Correlation matrix analyses were conducted to filter the input combinations into three different classes (M1, M2, and M3) based on the relationship between dependent and independent variables, and three performance metrics comprising the coefficient of determination (R2), Pearson correlation coefficient (PCC), and mean square error (MSE) were used to evaluate the prediction performance of the developed models in both calibration and validations stages. All four single models have very low R2 and PCC values (as low as 0.07) and very high MSE values (up to 4.92 wt %) for M1 and M2 in both calibration and validation phases. However, the ensemble ML models show R2 and PCC values of 0.99-1 and an MSE value of 0.01 wt % for M1, M2, and M3 input combinations. Therefore, ensemble paradigms improve the performance accuracy of single models by up to 58 and 62% in the calibration and validation phases, respectively. The ensemble paradigms predict with high accuracy the yield of ethylene and propylene in the catalytic cracking of crude oil and its fractions.
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Affiliation(s)
- A. G. Usman
- Department
of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138 Nicosia, Turkey
- Operational
Research Centre in Healthcare, Near East
University, 99138 Nicosia, Turkish Republic of
Northern Cyprus
| | - Abdulkadir Tanimu
- Center
for Refining and Advanced Chemicals, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - S. I. Abba
- Interdisciplinary
Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Selin Isik
- Department
of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138 Nicosia, Turkey
| | - Abdullah Aitani
- Center
for Refining and Advanced Chemicals, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
| | - Hassan Alasiri
- Center
for Refining and Advanced Chemicals, Research Institute, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
- Department
of Chemical Engineering, King Fahd University
of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
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Tabassum S, Abedin N, Rahman MM, Rahman MM, Ahmed MT, Islam R, Ahmed A. An online cursive handwritten medical words recognition system for busy doctors in developing countries for ensuring efficient healthcare service delivery. Sci Rep 2022; 12:3601. [PMID: 35246576 PMCID: PMC8897401 DOI: 10.1038/s41598-022-07571-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 02/21/2022] [Indexed: 11/09/2022] Open
Abstract
Doctors in developing countries are too busy to write digital prescriptions. Ninety-seven percent of Bangladeshi doctors write handwritten prescriptions, the majority of which lack legibility. Prescriptions are harder to read as they contain multiple languages. This paper proposes a machine learning approach to recognize doctors' handwriting to create digital prescriptions. A 'Handwritten Medical Term Corpus' dataset is developed containing 17,431 samples of 480 medical terms. In order to improve the recognition efficiency, this paper introduces a data augmentation technique to widen the variety and increase the sample size. A sequence of line data is extracted from the augmented images of 1,591,100 samples and fed to a Bidirectional Long Short-Term Memory (LSTM) network. Data augmentation includes pattern Rotating, Shifting, and Stretching (RSS). Eight different combinations are applied to evaluate the strength of the proposed method. The result shows 93.0% average accuracy (max: 94.5%, min: 92.1%) using Bidirectional LSTM and RSS data augmentation. This accuracy is 19.6% higher than the recognition result with no data expansion. The proposed handwritten recognition technology can be installed in a smartpen for busy doctors which will recognize the writings and digitize them in real-time. It is expected that the smartpen will contribute to reduce medical errors, save medical costs and ensure healthy living in developing countries.
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Affiliation(s)
- Shaira Tabassum
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.
| | - Nuren Abedin
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
| | | | - Md Moshiur Rahman
- Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | | | - Rafiqul Islam
- Global Communication Center, Grameen Communications, Dhaka, Bangladesh.,Medical Information Center, Kyushu University Hospital, Fukuoka, Japan
| | - Ashir Ahmed
- Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan.,Global Communication Center, Grameen Communications, Dhaka, Bangladesh
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Prediction Models of Early Childhood Caries Based on Machine Learning Algorithms. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18168613. [PMID: 34444368 PMCID: PMC8393254 DOI: 10.3390/ijerph18168613] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 08/11/2021] [Accepted: 08/12/2021] [Indexed: 11/17/2022]
Abstract
In this study, we developed machine learning-based prediction models for early childhood caries and compared their performances with the traditional regression model. We analyzed the data of 4195 children aged 1-5 years from the Korea National Health and Nutrition Examination Survey data (2007-2018). Moreover, we developed prediction models using the XGBoost (version 1.3.1), random forest, and LightGBM (version 3.1.1) algorithms in addition to logistic regression. Two different methods were applied for variable selection, including a regression-based backward elimination and a random forest-based permutation importance classifier. We compared the area under the receiver operating characteristic (AUROC) values and misclassification rates of the different models and observed that all four prediction models had AUROC values ranging between 0.774 and 0.785. Furthermore, no significant difference was observed between the AUROC values of the four models. Based on the results, we can confirm that both traditional logistic regression and ML-based models can show favorable performance and can be used to predict early childhood caries, identify ECC high-risk groups, and implement active preventive treatments. However, further research is essential to improving the performance of the prediction model using recent methods, such as deep learning.
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