🩺 Multiple Disease Prediction System

Multiple Disease Prediction is an advanced Data Science project that leverages machine learning to predict the likelihood of multiple diseases, including Kidney Disease, Liver Disease, and Parkinson’s Disease. The system aims to support early diagnosis, assist healthcare providers in decision-making, and reduce the time and cost of traditional diagnostics. The project integrates data preprocessing, machine learning model training, evaluation, and visualization using Streamlit and Power BI.

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🔧 Tech Stack

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📁 Project Structure

📂 multiple-disease-prediction
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├── 📁 app/                           # Streamlit application code
│   └── streamlit_app.py
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├── 📁 data/
│   ├── 📁 raw/                       # Original/raw datasets
│   │   ├── kidney_disease.csv
│   │   ├── liver_disease.csv
│   │   └── parkinsons_disease.csv
│   │
│   ├── 📁 cleaned/                   # Cleaned/preprocessed datasets
│   │   ├── kidney_disease_cleaned.csv
│   │   ├── liver_disease_cleaned.csv
│   │   └── parkinsons_disease_cleaned.csv
│
├── 📁 models/                        # Trained ML models (saved as pickle files)
│   ├── kidney_model.pkl
│   ├── liver_model.pkl
│   └── parkinsons_model.pkl
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├── 📁 notebooks/                     # google colab notebooks for EDA & modeling
│   ├── Kidney_Disease_Prediction.ipynb
│   ├── Liver_Disease_Prediction.ipynb
│   ├── Parkinsons_Prediction.ipynb
│
├── 📁 video/                         # Project demo video
│   └── project_demo.mp4
│
├── requirements.txt                  # Python dependencies
├── README.md                         # Project documentation
├── .gitignore                        # Ignore unnecessary files in git
└── LICENSE                           # Open-source license for project

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🚀 How to Run

1. Clone the repository

git clone https://github.com/Infant-Joshva/Project_4-Multiple-Disease-Prediction.git
cd Project_4-Multiple-Disease-Prediction

2. Install dependencies

pip install -r requirements.txt

3. Run the Streamlit app

streamlit run app/main.py

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📊 Features

• Multi-Disease Prediction: Predicts Kidney, Liver, and Parkinson’s disease probability based on user-provided symptoms, demographics, and test results.
• Data Preprocessing: Handles missing values, encodes categorical features, and scales numerical data to improve model accuracy.
• Machine Learning Models: Trained using Logistic Regression, Random Forest, and XGBoost for robust predictions.
• Interactive Streamlit App: Users can input personal health data and instantly receive disease probability and risk level.
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC, MAE, RMSE.
• Scalable & Secure: Designed to handle multiple users and ensure privacy of sensitive health data.
• Visual Insights: Graphs and charts showing feature importance, probability distributions, and high-risk patient identification.

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📷 App Screenshots

📄 Overview

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🩸 Kidney Disease

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🧬 Liver Disease

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🧩 Parkinsons Disease

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📚 Insights

• Patients with abnormal test results have higher predicted disease probabilities.
• Multi-disease prediction enables prioritizing early diagnosis and treatment.
• Visualizations improve interpretability of model results and risk analysis.
• Healthcare providers can monitor trends across patient populations and identify high-risk groups.

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👤 Author

Your Name
📧 [email protected]
🐙 GitHub
🔗 LinkedIn

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⭐ Give a Star!

If you find this project useful, please give it a ⭐ on GitHub!

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📜 License

This project is licensed under the MIT License - see the LICENSE file for details.