Family Doc: AI-Powered Personalized Healthcare Recommendation System

FamilyDoc is an AI-powered healthcare assistant developed to improve medical accessibility, diagnostic accuracy, and personalized treatment recommendations using Machine Learning (ML), Natural Language Processing (NLP), and AI-driven analytics. Traditional healthcare systems often struggle with delayed diagnoses, lack of personalization, and limited reach in rural areas. To address these issues, FamilyDoc integrates advanced ML models like Random Forest, Decision Trees, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs) for automated disease classification. Precision medicine techniques such as LASSO and Principal Component Analysis (PCA) are used to enhance treatment accuracy by identifying key patient attributes.In addition to disease prediction, FamilyDoc includes a specialized dosage recommendation system for patients with Diabetes, Blood Pressure (BP), and Cholesterol conditions, suggesting safer medication dosages based on their health profiles. A Patient History Module stores previous medication recommendations and allows users to download them as prescription-style PDFs or delete records for privacy. FamilyDoc also incorporates Federated Learning for secure, decentralized model training and supports multilingual NLP capabilities to serve diverse populations. Experimental evaluations show that Random Forest models achieve 98% accuracy in disease prediction, while deep learning architectures improve chronic disease monitoring through time-series data. With its AI-driven diagnostics, personalized treatment planning, and privacy-focused design, FamilyDoc aims to bridge the healthcare gap, especially in underserved rural communities.