• Saumyajeet Kumar
• Aditya Agniesh
• Shivam
• Keshav Jindal
• Shivansh

This research presents a novel online platform designed to revolutionize proactive health management through early disease prediction and personalized lifestyle recommendations. Utilizing a comprehensive dataset of over 5500 patient records, the platform employs advanced machine learning algorithms (SVM, logistic regression, gradient boosting, decision trees, and naive Bayes) to accurately identify 132 diseases based on individual symptom profiles. Seamlessly integrating predictive capabilities with user-friendly interfaces, the platform offers tailored interventions, including personalized workout routines, dietary plans, disease information, and age-specific recommendations. By providing this powerful tool freely, we aim to empower individuals to proactively monitor their health, detect potential risks, and make informed lifestyle choices, ultimately promoting wellness and improving population health outcomes. This research highlights the transformative potential of combining machine learning and user-centric design in preventative healthcare.

Personalized Health Recommendation , Machine learning, Support Vector Machine, Symptom prediction, Preventive Healthcare, Predictive modeling

"Intelligent Health Companion: A Web-Based System for Proactive Disease Detection and Lifestyle Recommendations", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.b243-b248, November-2024, Available :http://www.jetir.org/papers/JETIR2411127.pdf

"Intelligent Health Companion: A Web-Based System for Proactive Disease Detection and Lifestyle Recommendations", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppb243-b248, November-2024, Available at : http://www.jetir.org/papers/JETIR2411127.pdf