Heart Disease Prediction using Logistic Regression

This project implements a heart disease classification model using logistic regression from scratch, comparing both non-regularized and L2-regularized approaches. The objective is to predict whether a patient has heart disease based on clinical attributes.

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Overview

• Model: Logistic Regression (with and without L2 Regularization)
• Approach: Custom implementation using NumPy
• Evaluation: Accuracy, Cost Analysis, Decision Boundary Visualization
• Dataset: UCI Heart Disease dataset

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Dataset

The dataset used is the UCI Heart Disease Dataset. It consists of 303 patient records with the following attributes:

Key Features Used (after preprocessing):

• Age
• Sex
• Chest pain type
• Resting blood pressure
• Cholesterol level
• Fasting blood sugar
• Resting ECG results
• Maximum heart rate achieved
• Exercise-induced angina
• ST depression
• Slope of ST segment
• Number of major vessels
• Thalassemia

Target variable:

• 1: Presence of heart disease
• 0: Absence of heart disease

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Project Structure

heart_disease_logistic/
│
├── main.py # Main script to run both models
├── model_utils.py # Functions for cost, gradient, prediction
├── regularize_utils.py # Functions for L2-regularized cost and gradient
├── visual_utils.py # Plotting and decision boundary visualizations
├── heart.csv # Input dataset
├── README.md # Project documentation
└── requirements.txt # Python dependencies

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Results

Model	Training Accuracy	Test Accuracy	Final Cost
Logistic Regression	87.80%	80.98%	0.32
Regularized (λ = 0.01)	84.51%	80.00%	0.35

Observations:

• Regularization slightly reduced overfitting.
• The unregularized model achieved slightly higher training accuracy, but the regularized model had more stable generalization on unseen data.

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How to Run

1. Clone the Repository

git clone https://github.com/MansoobeZahra/Heart-Disease-Prediction.git
cd Heart-Disease-Prediction

2. Install Dependencies

pip install -r requirements.txt 3. Run the Model

python main.py

This will output training logs, final parameters, accuracy scores, and decision boundary plots.

Dependencies Python 3.7+

numpy

pandas

matplotlib

scikit-learn

You can install all required packages using:

pip install -r requirements.txt

License This project is licensed under the MIT License.

Connect

By Mansoob E Zehra

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