# CardioInsight: Machine Learning-Based Heart Risk Prediction

#programming #machinelearning #python #healthcare

CardioInsight: Machine Learning-Based Heart Risk Prediction

Project Overview

CardioInsight is an advanced machine learning project designed to predict cardiovascular risk in patients using clinical data. By analyzing key features such as age, cholesterol levels, and chest pain type, the system identifies high-risk individuals with remarkable accuracy, supporting early detection and preventive care. The project uses a Random Forest Classifier as the primary model along with feature selection techniques to ensure reliable and interpretable predictions.

Multiple models were trained and evaluated, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, and Random Forest, to select the best-performing model for deployment. Detailed evaluation metrics were recorded to compare performance and reliability.

Key Features

High-Performance Prediction: Achieves over 98% test accuracy with Random Forest.
Insightful Feature Analysis: Identifies the most significant clinical features affecting heart disease risk.
Data Visualization: Correlation heatmaps, feature importance plots, and model evaluation charts included.
Scalable & Modular: Easily adaptable to new datasets or alternative ML models.
Multi-Model Evaluation: Allows comparison of multiple models to select the most effective one.

Trained Models & Metrics

Model	Accuracy	Precision	Recall	F1-Score
Logistic Regression	86.6412	83.8509	93.75	88.5246
Decision Tree	97.3282	97.2414	97.9167	97.5779
Random Forest	98.4733	97.2973	100	98.6301
Gradient Boosting	96.9466	95.9459	98.6111	97.2603
SVM	89.6947	87.7419	94.4444	90.9699
KNN	87.0229	89.2857	86.8056	88.0282

Top Features

The following features were found to be most important in predicting heart disease risk:

Age
Cholesterol levels
Chest pain type (Typical angina)

How to Use

Clone the repository:

git clone https://github.com/Abubakar-Shabbir/HeartScope-Predictive-ML-for-Cardiovascular-Risk.git

Install the dependencies:

pip install -r requirements.txt

Open the Jupyter Notebook Navigate to the notebook file to explore data analysis, modeling, and visualizations: Notebooks/HeartScope_Predictive ML for Cardiovascular Risk.ipynb