Abstract
Abstract: This study proposes a multi-disease prediction system integrating assessments for diabetes, heart disease, and Parkinson’s disease. The system leverages machine learning algorithms like Logistic Regression, Support Vector Machines (SVMs), and K-Nearest Neighbors (KNN) to predict disease risk through a unified user interface. We delve into the system design process, emphasizing the importance of defining system compo- nents and exploring the utility of modeling languages. The research explores the potential of SVMs, including linear and non-linear variations, for disease prediction. We analyze existing literature on applying machine learning algorithms for disease prediction and discuss their potential for disease classification. Finally, the abstract addresses challenges and future directions in disease prediction, aiming to provide valuable insights for further research and development efforts.
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