Health Risk Prediction and Recommendation System Using Hybrid Machine Learning Models
DOI:
https://doi.org/10.62643/Keywords:
Machine Learning, Healthcare Analytics, Disease Prediction, Random Forest, Medical Diagnosis, Health Management System, PHP-Python Integration, Data Visualization, Predictive Analytics, Artificial Intelligence in Healthcare.Abstract
The rapid advancement of digital healthcare has paved the way for intelligent, data-driven
medical solutions. This paper presents an Integrated Health Management and Analytics System that
leverages advanced machine learning techniques to diagnose diseases, generate insights, and
recommend treatments based on patient symptoms. The system is built using a PHP-based frontend
with Python-powered machine learning for accurate disease prediction using the Random Forest
model. The model is trained on a comprehensive dataset containing various symptoms and their
associated diseases. The proposed system incorporates a role-based access model, allowing doctors,
patients, and healthcare administrators to interact with tailored interfaces. Patients can input their
symptoms, and the system processes the data in real time to provide diagnostic predictions, visual
analytics, and potential treatment recommendations. Unlike conventional electronic health record
(EHR) systems, this platform integrates real-time analytics while ensuring data privacy and security.
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