Machine Learning-Based Early Disease Prediction Using Electronic Health Records

Abstract

The increasing digitization of healthcare systems has led to the widespread adoption of Electronic Health Records (EHRs), offering unprecedented opportunities for early disease prediction and preventive healthcare. Machine learning (ML) techniques have emerged as powerful tools for analyzing complex and high-dimensional EHR data to identify patterns associated with disease onset. This study explores the application of ML algorithms for early disease prediction using structured and unstructured EHR data. It emphasizes predictive modeling approaches, feature engineering techniques, and model interpretability in clinical decision-making. The research highlights the effectiveness of supervised and deep learning models, including Random Forest, Gradient Boosting, and recurrent neural networks, in predicting chronic diseases such as diabetes, cardiovascular disorders, and respiratory illnesses. Furthermore, the study discusses challenges related to data quality, privacy, and model transparency. The findings suggest that ML-driven EHR analysis significantly enhances early detection capabilities, enabling proactive interventions and improving patient outcomes. The paper concludes by identifying future research directions aimed at integrating explainable AI, real-time data analytics, and multi-modal healthcare data for robust predictive healthcare systems.