Smart Health Diagnosis App is an AI-powered web application that predicts diseases based on user-input symptoms. It combines machine learning with an intuitive web interface to provide users with quick and accurate health insights.
- Enter symptoms via a clean web interface
- Disease prediction using a Random Forest classifier
- Fast AI-powered results
- Deployable on cloud platforms for global access
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Clone the repository: git clone https://github.com/Abhinavtiwari-doit/Smart_Health_Diagnosis_App.git
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Navigate to the directory: cd Smart_Health_Diagnosis_App
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Create and activate a virtual environment: python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate
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Install dependencies: pip install -r requirements.txt
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Run the app: python run.py
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Access locally at http://127.0.0.1:5000
Python, Flask, scikit-learn, Pandas, NumPy, joblib, Gunicorn, Render
Data preprocessing and model training scripts are under model_training/. Run train_model.py to train the model and generate files used in the app.
Users input symptoms, which are converted to feature vectors and fed into the Random Forest model to predict possible diseases. The backend uses Flask, serving predictions via API.
Random Forest classifier was chosen due to its robustness, accuracy on symptom-disease data, and ease of integration with scikit-learn.
Handling data inconsistencies, model generalization, and environment differences between local and cloud deployment.
- NLP-based symptom input parsing
- Expanded disease dataset
- Nearby hospital finder feature
MIT License