Cardiovascular Risk Prediction in Diabetes: A Hybrid Machine Learning Approach.

Abstract

Cardiovascular disease (CVD) is a major cause of morbidity and mortality in diabetic populations. Early detection of cardiovascular risk in diabetes is crucial to reduce complications, particularly in resource-limited settings. This study aimed to develop and evaluate a hybrid machine learning framework that integrates Long Short-Term Memory (LSTM) networks with traditional algorithms to improve cardiovascular risk prediction in diabetic patients. The hybrid model, which included structured data and time-series health data, was tested on a sample of 1,000 diabetes patients. Using 10-fold cross-validation, the model achieved impressive predictive performance (accuracy 98.7%, AUC 0.99). There are three main conclusions from this study. Initially, the hybrid model demonstrated a significant increase in CVD prediction accuracy when compared to independent machine-learning techniques. Second, the model provided reasonable predictions across different demographic groupings, ensuring equitable outcomes. Finally, the model's high performance supports its potential for future use in clinical decision-support systems aimed at improving outcomes and optimizing resource allocation. Increased CVD screening rates in diabetic patients, better access to care for communities with limited resources, and the advancement of health equity are all possible outcomes of incorporating machine learning and deep learning techniques. The proposed hybrid model also demonstrates strong potential for clinical deployment in cardiovascular risk prediction among diabetic populations, supporting earlier interventions and improved patient outcomes.