Machine Learning-Driven Medical Recommendation System for Early Disease Prediction and Personalized Treatment

Abstract

This project presents an intelligent Naive Bayes and K-Nearest Neighbors (KNN) based medical recommendation system to diagnose a disease based on symptoms provided by patients and prescribe personalized medicine.Using effective strategies such as feature encoding and balancing data; the system operates with high efficiency across different clinical data sets.Experimental results show the Naive Bayes model attains a striking accuracy rate of 96% compared to KNN and popular algorithms such as Random Forest because it operates smoothly with categorical features.The system not only efficiently diagnoses disease patterns with satisfactory accuracy but also effectively provides recommendations such as precautions, medicines, and lifestyle change and assists doctors with a robust decision-support system.This project explains how machine learning can enhance accuracy in diagnosis, reduce human mistakes, and benefit patients and how it provides future directions in developing AI-based health care solutions.

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