Heart Disease Prediction Using Logistic Regression

Overview

This project focuses on building a machine learning model to predict the presence of heart disease using Logistic Regression. It uses a dataset containing various medical attributes of patients and aims to assist in early diagnosis based on clinical data.

Objectives

• Predict the likelihood of heart disease in a patient
• Use logistic regression as a baseline classification model
• Analyze important health features contributing to the prediction

Dataset

• The dataset typically includes:
  • Age
  • Sex
  • Chest pain type
  • Resting blood pressure
  • Cholesterol
  • Fasting blood sugar
  • Resting ECG results
  • Maximum heart rate achieved
  • Exercise-induced angina
  • ST depression induced by exercise
  • Slope of peak exercise ST segment
  • Number of major vessels colored by fluoroscopy
  • Thalassemia
  • Target (0 = no disease, 1 = disease)

Tools & Libraries

• Python
• Pandas, NumPy – data handling
• Matplotlib, Seaborn – data visualization
• Scikit-learn – modeling and evaluation

Workflow

1. Data Loading & Exploration
   • Check for missing values and understand feature distributions.
2. Data Preprocessing
   • Encode categorical variables.
   • Feature scaling (if needed).
3. Modeling
   • Train a Logistic Regression model.
   • Evaluate performance using accuracy, precision, recall, and F1-score.
4. Visualization
   • Plot confusion matrix, ROC curve, feature correlations.

How to Run

1. Clone the repository:

   git clone https://github.com/your-username/heart-disease-prediction.git
   cd heart-disease-prediction

2. Install dependencies:

   pip install -r requirements.txt

3. Run the notebook:

   jupyter notebook

4. Open Heart Disease Prediction Using Logistic Regression.ipynb and follow along.

Results

• Logistic Regression achieved reasonable performance for baseline classification.
• Key indicators included chest pain type, cholesterol levels, and maximum heart rate.

Future Improvements

• Try other models: Random Forest, XGBoost, or Neural Networks.
• Perform hyperparameter tuning with GridSearchCV.
• Deploy the model using Flask or Streamlit for interactive predictions.

License

This project is licensed under the MIT License.