MediCare

💡 MediCare — Disease Prediction System

This project implements a Machine Learning-based Disease Prediction System designed to predict probable diseases based on a patient's symptoms. The core model is an optimized XGBoost Classifier (XGBClassifier), fine-tuned using RandomizedSearchCV for enhanced accuracy and performance.

⚙️ Tech Stack

• Language: Python

• Libraries:
  pandas, numpy, scikit-learn, xgboost, joblib, difflib, tabulate, scipy

🧠 Model Summary

• Model Used: XGBClassifier (eXtreme Gradient Boosting)

• Training Strategy:

  • Features selected using SelectKBest with mutual_info_classif.

  • Dataset split into training and testing sets using stratification.

  • Hyperparameters optimized via RandomizedSearchCV.

  • Final model saved as optimized_disease_predictor.pkl.

🔍 Features

• Interactive symptom input system with fuzzy matching (difflib) for typo correction.

• Dynamic prediction output showing:

  • Primary predicted disease with confidence score.

  • Alternative likely diseases.

• Symptom data structured for binary classification (1 = present, 0 = absent).

• Model trained on selected 40 most informative symptoms for high relevance and generalization.

💡 Prediction Output

• Predicts top three possible diseases with confidence percentages.

• Offers clear suggestions and highlights the need for professional medical consultation.