Paper Title
Predictive Health Analysis Through Symptoms Using Machine Learning Algorithms

Abstract
Predictive Health Analysis through Symptoms using Machine Learning is an innovative initiative poised to transform healthcare by leveraging machine learning to anticipate diseases based on early symptom reporting. By amalgamating diverse datasets, employing advanced machine learning algorithms, and deploying predictive modeling techniques, this project aims to facilitate early disease detection, assess individual disease susceptibility, and empower individuals and healthcare providers with proactive tools for preventive care. The potential impact of this endeavor lies in its capacity to drive early intervention, facilitate informed health management, and potentially optimize healthcare resource allocation by prioritizing preventive measures. The project evaluates the performance of three linear models—Na¨ıve Bayes, Support Vector Machine, and Random Forest—in disease prediction using datasets sourced from the UCI data repository. Furthermore, each algorithm is integrated into a prediction engine and exposed via an API. Additionally, a web platform has been developed to foster collaboration among researchers and healthcare professionals. The results demonstrate the prediction engine’s capability to accurately identify and anticipate the presence of diseases. Furthermore, there is room for performance enhancement through the utilization of more sophisticated machine learning techniques Keywords - Machine learning, Random Forest, Na¨ıve Bayes, Support Vector Machine (SVM).