🫀 Logistic Heart Disease Prediction

A comprehensive machine learning project using logistic regression to predict heart disease based on clinical and demographic features

📋 Table of Contents

• 🎯 Project Overview
• 🔬 Dataset Information
• 🚀 Features
• 🛠️ Technologies Used
• 📊 Model Performance
• 🏗️ Project Structure
• ⚙️ Installation & Setup
• 📖 Usage
• 🧪 Model Training Process
• 📈 Results & Analysis
• 🤝 Contributing
• 📄 License
• 👨‍💻 Author

🎯 Project Overview

Heart disease is one of the leading causes of death worldwide, making early detection and prediction crucial for preventive healthcare. This project implements a machine learning solution using Logistic Regression to predict the likelihood of heart disease in patients based on various clinical and demographic parameters.

🌟 Key Highlights

• Predictive Healthcare: Early detection system for heart disease risk assessment
• High Accuracy: Achieves 85%+ accuracy on test data
• Real-world Application: Based on authentic medical dataset with 303 patient records
• Interpretable Model: Logistic regression provides clear feature importance and probability scores
• Complete Pipeline: From data preprocessing to model deployment

🎯 Objectives

1. Develop a reliable machine learning model for heart disease prediction
2. Analyze the relationship between various medical factors and heart disease
3. Create an interpretable model that medical professionals can understand
4. Provide probability scores for risk assessment rather than just binary classification
5. Demonstrate the complete ML workflow from data exploration to model evaluation

🔬 Dataset Information

📊 Dataset Overview

Attribute	Value
Total Records	303 patients
Features	13 clinical/demographic variables
Target Classes	2 (Heart Disease: Yes/No)
Missing Values	None (Complete dataset)
File Format	CSV
Data Quality	High-quality medical data

🏥 Target Distribution

• Heart Disease Present (1): 165 patients (54.5%)
• Heart Disease Absent (0): 138 patients (45.5%)
• Balance: Well-balanced dataset suitable for binary classification

📋 Feature Descriptions

Feature	Type	Description	Clinical Significance
age	Numerical	Patient age (29-77 years)	Risk increases with age
sex	Binary	Gender (1=Male, 0=Female)	Different risk patterns by gender
cp	Categorical	Chest pain type (0-3)	Key symptom indicator
trestbps	Numerical	Resting blood pressure (mm Hg)	Major cardiovascular risk factor
chol	Numerical	Serum cholesterol (mg/dl)	High levels increase risk
fbs	Binary	Fasting blood sugar >120 mg/dl	Diabetes indicator
restecg	Categorical	Resting ECG results (0-2)	Heart electrical activity
thalach	Numerical	Maximum heart rate achieved	Exercise capacity indicator
exang	Binary	Exercise induced angina	Stress test result
oldpeak	Numerical	ST depression induced by exercise	ECG stress test measure
slope	Categorical	Slope of peak exercise ST segment	Exercise test parameter
ca	Numerical	Number of major vessels colored (0-3)	Coronary angiography result
thal	Categorical	Thalassemia type (1-3)	Blood disorder indicator

🚀 Features

✨ Core Functionality

• 🔍 Data Exploration: Comprehensive analysis of heart disease dataset
• 🧹 Data Preprocessing: Handling of categorical variables and feature scaling
• 🤖 Model Training: Logistic regression implementation with scikit-learn
• 📊 Model Evaluation: Multiple metrics including accuracy, precision, recall
• 🎯 Prediction System: Real-time heart disease risk assessment
• 📈 Visualization: Data distribution and model performance plots

🛡️ Model Capabilities

• Binary Classification: Predicts presence/absence of heart disease
• Probability Estimation: Provides confidence scores for predictions
• Feature Importance: Identifies most significant risk factors
• Cross-validation: Robust model validation techniques
• Interpretability: Clear understanding of model decisions

🛠️ Technologies Used

🐍 Core Technologies

Technology	Purpose	Version
Python	Primary programming language	3.7+
Pandas	Data manipulation and analysis	Latest
NumPy	Numerical computing	Latest
Scikit-learn	Machine learning library	Latest
Matplotlib	Data visualization	Latest
Seaborn	Statistical visualization	Latest
Jupyter Notebook	Interactive development	Latest

📦 Dependencies

numpy>=1.19.0
pandas>=1.2.0
scikit-learn>=0.24.0
matplotlib>=3.3.0
seaborn>=0.11.0
jupyter>=1.0.0

📊 Model Performance

🎯 Performance Metrics

Metric	Training Data	Testing Data
Accuracy	~85-90%	~85%+
Precision	High	Consistent
Recall	High	Consistent
F1-Score	Balanced	Stable

📈 Key Performance Indicators

• ✅ High Accuracy: Consistent 85%+ accuracy across train/test splits
• ⚖️ Balanced Performance: Good precision-recall balance
• 🎯 Generalization: Minimal overfitting, stable test performance
• 🔄 Reproducibility: Consistent results with random state control

🏗️ Project Structure

Logistic-Heart-Disease-Prediction/
├── 📊 heart_disease_data.csv           # Dataset file
├── 📓 Logistic-Heart-Disease-Prediction.ipynb  # Main notebook
├── 📖 README.md                        # This file
├── 📄 LICENSE                          # License information
└── 📁 docs/                           # Documentation
    ├── 📋 dataset.md                   # Dataset documentation
    └── 🤖 logistic-regression-model.md # Model documentation

📁 File Descriptions

• heart_disease_data.csv: Complete dataset with 303 patient records
• Logistic-Heart-Disease-Prediction.ipynb: Main Jupyter notebook with complete analysis
• docs/dataset.md: Detailed dataset documentation and feature descriptions
• docs/logistic-regression-model.md: Comprehensive model documentation
• README.md: Project overview and instructions
• LICENSE: License terms and conditions

⚙️ Installation & Setup

🔧 Prerequisites

• Python 3.7+ installed on your system
• Git for version control
• Jupyter Notebook or JupyterLab
• Code Editor (VS Code, PyCharm, etc.)

📥 Quick Setup

1. Clone the Repository

   git clone https://github.com/NhanPhamThanh-IT/Logistic-Heart-Disease-Prediction.git
   cd Logistic-Heart-Disease-Prediction
   

2. Create Virtual Environment (Recommended)

   # Windows
   python -m venv venv
   venv\Scripts\activate
   
   # macOS/Linux
   python3 -m venv venv
   source venv/bin/activate
   

3. Install Dependencies

   pip install numpy pandas scikit-learn matplotlib seaborn jupyter
   

4. Launch Jupyter Notebook

   jupyter notebook
   

5. Open the Main Notebook

   • Navigate to Logistic-Heart-Disease-Prediction.ipynb
   • Run all cells to see the complete analysis

📖 Usage

🚀 Quick Start

1. Open the Jupyter Notebook

   jupyter notebook Logistic-Heart-Disease-Prediction.ipynb
   

2. Run the Complete Analysis

   • Execute all cells in sequence
   • Observe data exploration, model training, and evaluation

3. Make Predictions

   # Example prediction for a new patient
   input_data = (62, 0, 0, 140, 268, 0, 0, 160, 0, 3.6, 0, 2, 2)
   prediction = model.predict([input_data])
   
   if prediction[0] == 0:
       print('No Heart Disease Detected')
   else:
       print('Heart Disease Risk Detected')
   

🎯 Making Custom Predictions

To predict heart disease for a new patient, provide the following 13 features in order:

# Feature order: age, sex, cp, trestbps, chol, fbs, restecg,
#                thalach, exang, oldpeak, slope, ca, thal

# Example: 62-year-old female with specific medical parameters
patient_data = (62, 0, 0, 140, 268, 0, 0, 160, 0, 3.6, 0, 2, 2)
risk_prediction = model.predict([patient_data])
confidence_score = model.predict_proba([patient_data])

🧪 Model Training Process

📊 Step-by-Step Workflow

1. 📥 Data Loading

   • Load heart disease dataset from CSV
   • Initial data inspection and validation

2. 🔍 Exploratory Data Analysis

   • Statistical summary of features
   • Target variable distribution analysis
   • Missing value assessment

3. ⚙️ Data Preprocessing

   • Feature-target separation
   • Train-test split (80-20 ratio)
   • Stratified sampling for balanced splits

4. 🤖 Model Training

   • Logistic Regression initialization
   • Model fitting on training data
   • Parameter optimization

5. 📈 Model Evaluation

   • Training accuracy assessment
   • Testing accuracy evaluation
   • Performance metric calculation

6. 🎯 Prediction System

   • Individual patient prediction
   • Probability score generation
   • Risk assessment interpretation

🎛️ Model Configuration

# Model Parameters
model = LogisticRegression(
    random_state=42,        # Reproducibility
    max_iter=1000,         # Convergence assurance
    solver='liblinear'     # Optimal for small datasets
)

# Train-Test Split Configuration
X_train, X_test, Y_train, Y_test = train_test_split(
    X, Y,
    test_size=0.2,         # 20% for testing
    stratify=Y,            # Maintain class balance
    random_state=2         # Reproducible splits
)

📈 Results & Analysis

🎯 Model Performance Summary

• ✅ Training Accuracy: ~85-90% (Indicates good learning)
• ✅ Testing Accuracy: ~85%+ (Demonstrates generalization)
• ⚖️ Balanced Performance: No significant overfitting
• 🎯 Consistent Results: Stable across different random states

📊 Key Insights

1. 🫀 Age Factor: Strong correlation with heart disease risk
2. 👥 Gender Differences: Distinct risk patterns between males and females
3. 💓 Chest Pain: Critical symptom