Predictive Modeling for Early Disease Diagnosis Using Machine Learning: A Healthcare Data-Driven Approach

Abstract

The application of machine learning (ML) in healthcare has transformed the way clinical data is analyzed and utilized for patient care. This study investigates the effectiveness of predictive Modeling using ML algorithms for early disease diagnosis, focusing on chronic conditions such as diabetes, cardiovascular diseases, and cancer. Leveraging large-scale electronic health records (EHRs), we implemented and compared multiple supervised learning models, including Random Forest, Support Vector Machine (SVM), and Gradient Boosting, to predict disease onset based on clinical parameters and patient history. The models were evaluated based on accuracy, precision, recall, and F1-score, with Gradient Boosting demonstrating superior performance in most scenarios. Our findings highlight the potential of ML in enhancing diagnostic accuracy, enabling earlier intervention, and ultimately improving patient outcomes. The study underscores the importance of data quality, feature selection, and algorithm interpretability in healthcare ML applications. Future research should focus on integrating real-time data and improving model generalizability across diverse populations.