https://github.com/allanotieno254/heart-disease-prediction-using-artificial-neural-networks-ann-
This project aims to develop an Artificial Neural Network (ANN) model to predict the likelihood of heart disease based on multiple health-related features.
https://github.com/allanotieno254/heart-disease-prediction-using-artificial-neural-networks-ann-
ai ann data-processing jupyter-notebook ml model-evaluation-metrics python
Last synced: 7 months ago
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This project aims to develop an Artificial Neural Network (ANN) model to predict the likelihood of heart disease based on multiple health-related features.
- Host: GitHub
- URL: https://github.com/allanotieno254/heart-disease-prediction-using-artificial-neural-networks-ann-
- Owner: AllanOtieno254
- Created: 2025-03-07T18:25:39.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-07T18:28:01.000Z (7 months ago)
- Last Synced: 2025-03-07T19:28:37.842Z (7 months ago)
- Topics: ai, ann, data-processing, jupyter-notebook, ml, model-evaluation-metrics, python
- Language: Jupyter Notebook
- Homepage: https://colab.research.google.com/drive/1SDAtt94HMB1w_vV8_0uIcisDz4pFILNf?usp=sharing
- Size: 53.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Heart Disease Prediction using Artificial Neural Networks (ANN)
## 📌 Project Overview
This project aims to develop an **Artificial Neural Network (ANN) model** to predict the likelihood of heart disease based on multiple health-related features. The dataset used contains key health indicators such as age, cholesterol levels, blood pressure, and more. The goal is to build a model that assists healthcare professionals in early diagnosis and risk assessment.
---
## 📊 Dataset Information
- **Name:** Heart Disease Dataset
- **Source:** UCI Machine Learning Repository
- **Features:**
- Age
- Sex
- Chest Pain Type (CP)
- Resting Blood Pressure (trestbps)
- Serum Cholesterol (chol)
- Fasting Blood Sugar (fbs)
- Resting Electrocardiographic Results (restecg)
- Maximum Heart Rate Achieved (thalach)
- Exercise Induced Angina (exang)
- ST Depression Induced by Exercise (oldpeak)
- Slope of the Peak Exercise ST Segment (slope)
- Number of Major Vessels (ca)
- Thalassemia (thal)
- **Target Variable:** Presence of heart disease (1 = Yes, 0 = No)
---
## 🚀 Technologies Used
- **Python** (Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow/Keras)
- **Jupyter Notebook/Google Colab**
- **Artificial Neural Networks (ANN)**
- **Data Preprocessing Techniques**
- **Model Evaluation Metrics**
---
## ⚙️ Installation & Setup
To run this project, follow these steps:
### **1️⃣ Clone the Repository**
```bash
git clone https://github.com/your-username/Heart-Disease-Prediction-ANN.git
cd Heart-Disease-Prediction-ANN
```
### **2️⃣ Create a Virtual Environment (Optional but Recommended)**
```bash
python -m venv env
source env/bin/activate # On Mac/Linux
env\Scripts\activate # On Windows
```
### **3️⃣ Install Dependencies**
```bash
pip install -r requirements.txt
```
---
## 📖 Project Workflow
1. **Data Preprocessing**
- Load dataset
- Handle missing values
- Feature scaling using StandardScaler
- Train-test split
2. **Model Training**
- Define an ANN model using TensorFlow/Keras
- Compile and train the model
- Evaluate performance using accuracy and loss
3. **Model Prediction**
- Save the trained model
- Load the model for predictions
- Predict heart disease risk on new data
---
## 🏗️ Model Architecture
```python
model = Sequential([
Dense(32, activation='relu', input_shape=(X_train.shape[1],)),
Dropout(0.5),
Dense(64, activation='relu'),
Dropout(0.1),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=50, batch_size=16, validation_data=(X_test, y_test))
```
---
## 📊 Model Evaluation
- Training Accuracy: **~95%**
- Validation Accuracy: **~90%**
- Loss Function: Binary Crossentropy
---
## 📈 Results
- The model successfully predicts heart disease risk with high accuracy.
- It identifies key risk factors contributing to heart disease.
- Future improvements: Fine-tuning hyperparameters, adding more layers, testing with larger datasets.
---
## 📌 How to Use the Model for Predictions
### **Step 1: Load the Trained Model**
```python
from tensorflow.keras.models import load_model
model = load_model("models/heart_disease_model.h5")
```
### **Step 2: Prepare New Data**
```python
import numpy as np
new_patient = np.array([[63, 1, 3, 145, 233, 1, 0, 150, 0, 2.3, 0, 0, 1]]) # Example input
data_scaled = scaler.transform(new_patient)
```
### **Step 3: Make Predictions**
```python
prediction = model.predict(data_scaled)
predicted_class = (prediction > 0.5).astype(int)
print("Predicted Heart Disease Risk:", predicted_class)
```
---
## 📜 License
This project is licensed under the **MIT License**.
---
## 🤝 Contributing
We welcome contributions! Feel free to fork the repository and submit pull requests.
---
## 📬 Contact
- **Author:** Allan Otieno Akumu
- **GitHub:** [AllanOtieno254](https://github.com/AllanOtieno254)
- **Email:** allanotieno2001@gmail.com