Predicting Diabetes in Patients with Metabolic Syndrome Using Machine-Learning Model Based on Multiple Years’ Data

Li, Jing; Xu, Zheng; Xu, Tianming; Lin, Shen

doi:10.2147/dmso.s381146

Cited by 3 publications

References 28 publications

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Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach

Acheampong,

Adua,

Obirikorang

et al. 2024

J Diabetes Metab Disord

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Objectives The burgeoning prevalence of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) within Africa is concerning. Machine learning (ML) techniques offer a unique opportunity to leverage data-driven insights and construct predictive models for MetS risk, thereby enhancing the implementation of personalised prevention strategies. In this work, we employed ML techniques to develop predictive models for pre-MetS and MetS among diabetic patients. Methods This multi-centre cross-sectional study comprised of 919 T2DM patients. Age, gender, novel anthropometric indices along with biochemical measures were analysed using BORUTA feature selection and an ensemble majority voting classification model, which included logistic regression, k-nearest neighbour, Gaussian Naive Bayes, Gradient boosting classification, and support vector machine. Results Distinct metabolic profiles and phenotype clusters were associated with MetS progression. The BORUTA algorithm identified 10 and 16 significant features for pre-MetS and MetS prediction, respectively. For pre-MetS, the top-ranked features were lipid accumulation product (LAP), triglyceride-glucose index adjusted for waist-to-height ratio (TyG-WHtR), coronary risk (CR), visceral adiposity index (VAI) and abdominal volume index (AVI). For MetS prediction, the most influential features were VAI, LAP, waist triglyceride index (WTI), Very low-density cholesterol (VLDLC) and TyG-WHtR. Majority voting ensemble classifier demonstrated superior performance in predicting pre-MetS (AUC = 0.79) and MetS (AUC = 0.87). Conclusion Identifying these risk factors reveals the complex interplay between visceral adiposity and metabolic dysregulation in African populations, enabling early detection and treatment. Ethical integration of ML algorithms in clinical decision-making can streamline identification of high-risk individuals, optimize resource allocation, and enable precise, tailored interventions.

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Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach

Acheampong,

Adua,

Obirikorang

et al. 2024

J Diabetes Metab Disord

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Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review

Daza,

Olivos-López,

Chumbirayco Pizarro

et al. 2024

Informatics in Medicine Unlocked

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Data-driven Approach to Age Prediction on Patients Diabetes and Cardiovascular Diseases Using Machine Learning: National Health and Nutrition Health Survey (Nhanes)

Abbas

2023

Preprint

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Background Diabetes and cardiovascular disease are two of the main causes of death in the United States. Identifying and predicting these diseases in patients is the first step towards stopping their progression. We evaluate the capabilities of machine learning models in detecting at-risk patients using survey data (and laboratory results), and identify key variables within the data contributing to these diseases among the patients. Methods Our research explores data-driven approaches which utilize supervised machine learning models to identify patients with such diseases. Using the National Health and Nutrition Examination Survey (NHANES) dataset, we conduct an exhaustive search of all available feature variables within the data to develop models for cardiovascular, prediabetes, and diabetes detection. Using different time-frames and feature sets for the data (based on laboratory data), multiple machine learning models (Support vector machines and adaptive boosting) were evaluated on their classification performance. The models were then combined to develop a weighted ensemble model, capable of leveraging the performance of the disparate models to improve detection accuracy. Information gain of tree-based models was used to identify the key variables within the patient data that contributed to the detection of at-risk patients in each of the diseases classes by the data-learned models. Results Diabetes and cardiovascular disease (CVD) are two of the leading causes of death in the United States. Detecting and predicting these diseases in patients is the first step to halting their progression. In this study, it was used Adaptive Boosting (AdaBoost) and Support Vector Machines (SVM) together as prediction. The purpose of this study was to knowing whether AdaBoost SVM could produce good accuracy. Tests were conducted using 50% data training and 50% data testing. Dot kernel were used to SVM. The highest accuracy value of AdaBoost SVM was accuracy 98.54%. Therefore it could be that AdaBoost can improve the performance of SVM in prediction of CVD desease severity Conclusion We conclude machine learned models based on survey questionnaire can provide an automated identification mechanism for patients at risk of diabetes and cardiovascular diseases. We also identify key contributors to the prediction, which can be further explored for their implications on electronic health records.

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Predicting Diabetes in Patients with Metabolic Syndrome Using Machine-Learning Model Based on Multiple Years’ Data

Cited by 3 publications

References 28 publications

Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach

Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach

Clinical applications of artificial intelligence in diabetes management: A bibliometric analysis and comprehensive review

Data-driven Approach to Age Prediction on Patients Diabetes and Cardiovascular Diseases Using Machine Learning: National Health and Nutrition Health Survey (Nhanes)

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