Multiple Disease Prediction System Using Machine Learning

Abstract The rapid advancement of machine learning algorithms and the exponential growth of healthcare data provide unprecedented opportunities for automated disease prediction. This paper proposes a comprehensive multiple disease prediction system utilizing diverse machine learning models to analyze patient symptoms, clinical data, and laboratory results for accurate simultaneous detection of diseases including diabetes, cardiovascular ailments, and liver disorders. Our approach leverages state-of-the-art preprocessing, feature selection, and ensemble learning to maximize predictive performance. Experimental evaluation on benchmark datasets demonstrates that the proposed system achieves an accuracy surpassing 89%, with balanced precision and recall metrics crucial for clinical relevance. This system aims to augment clinical decision-making by providing early warning of multiple diseases, thereby improving patient care outcomes and reducing diagnostic delays.