Health Insights: Orchestrating Holistic Wellness Through Symptom- Based Solutions
Yash Vinod Patil
Department of Information Technology
Shah & Anchor Kutchhi Engineering College Mumbai, India
yash.patil16431@sakec.ac.in
Dhanraj Sawant
Department of Information Technology
Shah & Anchor Kutchhi Engineering College Mumbai, India dhanaraj.sawant16567@sakec.ac.in
Nivedeeta Mukherjee
Department of Information Technology
Shah & Anchor Kutchhi Engineering College Mumbai, India nivedeeta.mukherjee@sakec.ac.in
Aditya Gupta
Department of Information Technology
Shah & Anchor Kutchhi Engineering College Mumbai, India aditya.gupta16419@sakec.ac.in
Shivam Vibhute
Department of Information Technology
Shah & Anchor Kutchhi Engineering College Mumbai, India shivam.vibhute16857@sakec.ac.in
Abstract
Access to timely and quality healthcare remains a significant global challenge, especially in regions with a shortage of healthcare professionals and limited medical infrastructure. Health Insights is a Flask-based web application designed to bridge this gap by providing a user- friendly, AI-powered healthcare management system. The system facilitates secure patient and doctor registration, appointment scheduling, and an AI-driven disease prediction module based on a Random Forest Classifier trained on a medical dataset containing 4,920 patient records and 132 symptoms. The model achieved 100% accuracy in predicting 41 different diseases during validation, demonstrating its potential as adiagnostic aid. Beyond disease prediction, Health Insights integrates a mental health analysis module and a lung cancer risk assessment system using machine learning techniques.
Keywords – Machine Learning, Personalized Healthcare, Disease Prediction, Flask Framework, Artificial Intelligence in Healthcare, Medical Recommendation System, Decision Tree Classifier, Random Forest Classifier, Support Vector Machine (SVM), Health Informatics.
