Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction

Abstract

Chronic diseases, a global public health challenge, necessitate the deployment of cutting-edge predictive models for early diagnosis and personalized interventions. This study presents an advanced methodology for early prediction of chronic diseases, including heart attack, diabetes, breast cancer, and kidney disease, leveraging a synergistic combination of cutting-edge techniques. Recognizing the challenge posed by extensive medical datasets with numerous features, we introduce a novel approach that begins with Feature Engineering using Recursive Feature Elimination (RFE) in conjunction with a Support Vector Machine (SVM). The presented methodology identifies and removes irrelevant features to simplify data complexity. The refined dataset is then input into the robust eXtreme Gradient Boosting (XGBoost) classifier, known for its efficiency and adeptness in predicting complex relationships within the data. The chosen ensemble learning algorithm demonstrates significant prowess in inducing intricate patterns crucial for chronic disease prediction. To enhance model performance, an essential phase of optimization is introduced through hyperparameter tuning using Bayesian optimization. This strategically navigates the hyperparameter space, maximizing the efficiency of the search process and fine-tuning the model for optimal predictive accuracy. The proposed approach showcases a substantial improvement in the early prediction of chronic diseases, demonstrating the effectiveness of the proposed approach.