https://github.com/piyushjimiwal/heart_disease_prediction

A Neural Network-based heart disease prediction model using TensorFlow and Scikit-learn
https://github.com/piyushjimiwal/heart_disease_prediction

classification healthcare heart-disease keras machine-learning neural-network python student-project tensorflow

Last synced: 4 months ago
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A Neural Network-based heart disease prediction model using TensorFlow and Scikit-learn

• Host: GitHub
• URL: https://github.com/piyushjimiwal/heart_disease_prediction
• Owner: PiyushJimiwal
• License: mit
• Created: 2025-06-23T17:32:38.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-06-24T16:36:18.000Z (4 months ago)
• Last Synced: 2025-06-24T16:43:00.050Z (4 months ago)
• Topics: classification, healthcare, heart-disease, keras, machine-learning, neural-network, python, student-project, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 278 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# ❤️ Heart Disease Prediction using Neural Networks

This project uses machine learning to predict the presence of heart disease in patients based on various medical attributes. A neural network model has been trained on the **Cleveland Heart Disease dataset** and achieves an accuracy of **88%**.

---

## 📌 Project Highlights

- ✅ Dataset: [Heart Disease - Cleveland UCI Dataset](https://www.kaggle.com/datasets/cherngs/heart-disease-cleveland-uci)

- ✅ Model: Neural Network built with TensorFlow/Keras

- ✅ Accuracy: **88%**

- ✅ Language: Python

- ✅ Preprocessing: Missing value handling, encoding, and scaling

- ✅ Evaluation: Accuracy, loss curves

---

## ⚙️ Model

- Framework: **TensorFlow / Keras**

- Type: Feedforward Neural Network

- Preprocessing:

  - Replaced missing values with `NaN` then filled with column **medians**

  - One-hot encoding for categorical features

  - Feature scaling using `StandardScaler`

## 📊 Features Used

- Age

- Sex

- Chest Pain Type

- Resting Blood Pressure

- Serum Cholesterol

- Fasting Blood Sugar

- Resting ECG

- Maximum Heart Rate Achieved

- Exercise Induced Angina

- ST depression induced by exercise

- Slope of peak exercise ST segment

- Number of major vessels

- Thalassemia

---

## License

This project is licensed under the [MIT License](LICENSE).

## 🚀 Getting Started

1. Clone the repository:

   ```bash

   git clone https://github.com/PiyushJimiwal/Heart_Disease_prediction.git

   cd Heart_Disease_prediction