Personalized Health Recommendation System using Machine Learning

Abstract

Abstract: The core objective of the project is to develop a health website capable of harnessing individual health data to generate personalized health recommendations, fostering early detection and more effective management of health issues. Utilizing Machine Learning (ML), the team aims to predict diseases like arrhythmia, Sleep Apnea, Insomnia, and Stroke, pivotal for early intervention and adapting diagnosis strategies. In their approach, ML algorithms, including Logistic Regression, Random Forest, and Voting classifier, analyze diverse health data sources to build a comprehensive recommendation system. It is noteworthy that all models exhibit an accuracy exceeding 90%, which underscores the reliability and effectiveness of the system. By seamlessly integrating various health data sources and emphasizing proactive health management, this initiative holds transformative potential, empowering individuals to make informed decisions about their well-being and promoting improved health outcomes.