Hybrid machine learning framework for chronic disease risk assessment - Shadaksharappa, Harini; K. B., Rashmi; D. K., Shreyas; Mikali, Somanath; Gowda, Vishesh P.; C. A., Uday Shankar; Iyerr, Siddarth B. (International Journal of Electrical and Computer Engineering (IJECE), 2026)
Chronic diseases like asthma, diabetes, stroke, and heart disease are the major causes of morbidity globally, which emphasizes the need for efficient predictive models to facilitate early detection and precautionary measures. Previous studies have used machine learning approaches for single-disease prediction, where models are designed for specific diseases, such as diabetes or heart disease. However, very few attempts have been made to develop unified frameworks for predicting multiple diseases simultaneously. This work presents a novel, unified framework using an ensemble of extreme gradient boosting classifier (XGBClassifier) and artificial neural networks (ANN) as individual classifiers to concurrently predict the risk of developing asthma, diabetes, stroke, and heart disease. This work follows a questionnaire-based approach that utilizes demographic, lifestyle, health metrics, symptoms and exposure-related data to create personalized risk assessments. The model achieves satisfactory accuracy rates of 95.82% for asthma, 96.68% for diabetes, 94.91% for stroke, and 94.52% for heart disease. The findings highlight how this novel hybrid model serves as an effective approach to tackle the intricate interactions between chronic ailments. The research also includes a user-friendly website that comprises a questionnaire and makes use of the best performing model to predict the probabilities of developing different diseases.