A Machine Learning Approach for Risk Stratification of Cardiovascular Disease in Type 2 Diabetic Cohort

Abstract

Cardiovascular disease (CVD) is an important complication of Type 2 diabetes, resulting in high morbidity and mortality. The early diagnosis of this condition is vital for appropriate treatment, but traditional marker-based diagnostics tend to lack sensitivity in the early phases. This research study uses different machine learning approaches to enhance the early diagnosis of CVD among Type 2 diabetic patients. The sample 703 patients data was collected through cross sectional study carried out within five months from May 2024 to September 2024 at one of the tertiary care hospitals in Karachi, Pakistan, using stratified random sampling. The features include sex, age, BMI, HbAIc, creatinine, cholesterol, triglycerides, History of stroke and myocardial infarction. Patients were grouped on basis of their disease status. Descriptive analysis showed clear patterns of poor health indicators with CVD group. Machine learning models including Logistic regression, Support Vector Machine and Random Forest were implemented and evaluated. These models showed promising results, especially in identifying patients at risk of CVD. In conclusion, Random Forest, demonstrated higher accuracy in 80:20 split over other models validating the feature importance. There are still some limitations to overcome such as interpretability and class imbalance; however, these models provide substantial advancement over conventional diagnostic methods.

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