HeartGuard AI

Heart Disease Prediction using Machine Learning

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Heart disease prevention has become essential for public health. Data-driven systems for predicting heart disease can greatly enhance research and preventive care, helping more people live healthier lives. This is where Machine Learning and Artificial Intelligence come into play, making accurate predictions for heart disease risk possible.

Project Overview

HeartGuard AI analyzes patient data to predict heart disease risk. The project includes:

• Data Processing: Comprehensive data cleaning and feature engineering.
• Model Training: Training various machine learning models.
• Prediction Accuracy: Accurate predictions using advanced algorithms.

The project was developed in a Jupyter Notebook using a heart disease dataset.

Objective

The goal is to predict the presence of heart disease in patients based on several health parameters. This is a binary classification problem:

• Input Features: Various health metrics and lifestyle parameters.
• Target Variable: Binary output indicating the presence or absence of heart disease.

Machine Learning Algorithms Used

We implemented a range of machine learning algorithms in Python to build the prediction model:

• Logistic Regression (Scikit-learn)
• Naive Bayes (Scikit-learn)
• Support Vector Machine (SVM) (Scikit-learn)
• K-Nearest Neighbors (KNN) (Scikit-learn)
• Decision Tree (Scikit-learn)
• Random Forest (Scikit-learn)
• XGBoost (Scikit-learn)
• Artificial Neural Network (Keras) - Single hidden layer

Best Accuracy Achieved: 90.16% (using Random Forest)

Project Workflow

1. Data Preprocessing: Cleaned and prepared data for model training.
2. Feature Engineering: Extracted meaningful features using TF-IDF and other techniques.
3. Model Training: Tested multiple algorithms to optimize predictive accuracy.
4. Evaluation: Evaluated models to identify the best-performing one (Random Forest).

Dataset Information

The dataset includes multiple attributes related to patient health, such as age, cholesterol levels, blood pressure, and more.

Results

HeartGuard AI achieved a maximum prediction accuracy of 95% with the Random Forest algorithm, demonstrating strong potential for real-world application in predictive healthcare systems.

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