Key result
A Random Forest machine learning model predicted the occurrence of T2DM, CAD, and their co-occurrence with an overall accuracy of 76% and an AUC of 0.95.
Why the study?
Type 2 diabetes mellitus and coronary artery disease place a significant burden on healthcare, motivating the implementation of machine learning models to predict disease occurrence and identify key variables.
Can machine learning models accurately predict the occurrence of T2DM, CAD, and their co-occurrence using biochemical, demographic, and physical data?
Observational (n=123)
Can machine learning models accurately predict the occurrence of T2DM, CAD, and their co-occurrence using biochemical, demographic, and physical data?
Effect estimate: AUC 0.95
Machine learning algorithms, particularly Random Forest, can accurately predict the prevalence and co-occurrence of T2DM and CAD using biochemical and demographic factors.
ML models may support T2DM and CAD prediction; leaves open prospective validation before clinical use.
Background Non-communicable diseases such as type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD) are causing a significant burden on the human health care system. The present study aims to implement a machine learning (ML) based predictive model for T2DM, CAD and their co-occurrence, followed by identifying key variables for the occurrence of disease. Methods The study is a data driven approach that applied various supervised ML models to predict the disease occurrence based on biochemical, demographic and physical data collected from 123 subjects. In addition, performance metrics like accuracy and AUC were used to evaluate the classification accuracy of ML models. Results The data (n = 123 subjects) consist of male (83) and female (40) populace with ages from 35 and 70 years. Among all ML models, the Random Forest was outperformed with an accuracy of 76% (AUC:0.95). Similarly, in T2DM (AUC:0.92) and T2DM + CAD (AUC:0.94) classification, Random Forest achieved the highest accuracy followed by logistic regression for CAD data (AUC:0.98). The major risk factors recognized for T2DM, CAD and T2DM + CAD are HbA1c, FBS, CK-MB, APO.AII, APO.E, IP.10, and total cholesterol. Conclusion The results suggest that the ML algorithms predict the prevalence of T2DM, CAD and concomitance in the populace through the integration of biochemical, physical and demographic factors. Based on the identified risk contributors for disease occurrence, preventive measures can be drafted to reduce the disease burden.
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Jangili et al. (2023) conducted an observational in Type 2 diabetes mellitus and coronary artery disease (n=123). Machine learning predictive models (Random Forest) vs. Other machine learning models was evaluated on Classification accuracy and AUC for disease occurrence (AUC 0.95). A Random Forest machine learning model predicted the occurrence of T2DM, CAD, and their co-occurrence with an overall accuracy of 76% and an AUC of 0.95.
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