ArticlePeerJ2020
A multi-class classification model for supporting the diagnosis of type II diabetes mellitus.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Machine Learning Classification Models for the Prediction of Type 2 Diabetes Mellitus: A Systematic Survey and Meta-Analysis Approach.International journal of environmental research and public health · 2022Pooled it
- Prediction of Diabetes Among Homeless Adults Using Artificial Intelligence: Suggested Recommendations.Healthcare (Basel, Switzerland) · 2026Article
- A machine learning approach for type 2 diabetes diagnosis and prognosis using tailored heterogeneous feature subsets.Medical & biological engineering & computing · 2025Article
- Predictive model for early detection of type 2 diabetes using patients' clinical symptoms, demographic features, and knowledge of diabetes.Health science reports · 2024Article
- Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics.PloS one · 2024Article
- Life-Course Multidisciplinary Psychosocial Predictors of Dementia Among Older Adults: Results From the Health and Retirement Study.Innovation in aging · 2024Article
- Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques.Journal of personalized medicine · 2022Article
- Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.Diabetology & metabolic syndrome · 2021Review
- Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy.Entropy (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
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Abstract
backgroundNumerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided.
methodsThis study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance.
resultsThe results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.
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