🌍 AI for SDG 3: Predicting Heart Disease Risk

Project Pitch Deck

🎯 Project Overview

This project uses a Machine Learning model (Logistic Regression) to predict the likelihood of heart disease using basic health indicators such as age, cholesterol, blood pressure, and heart rate.

It supports UN Sustainable Development Goal 3: Good Health and Well-being, by showing how AI can assist in early disease detection and preventive healthcare.

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🧠 Machine Learning Approach

• Algorithm: Logistic Regression
• Type: Supervised Learning (Binary Classification)
• Tools: Python, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
• IDE: Visual Studio Code (Ubuntu)

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📊 Dataset

• Source: : Using local dataset .
• Features: 13 clinical and demographic attributes
• Target: target (1 = has heart disease, 0 = healthy)

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⚙️ Installation & Running the Project

Step 1: Clone this repo

git clone https://github.com/toxidity-18/SDG13-HeartDiseasePrediction-.git
cd SDG3_HeartDisease_Prediction

Step 2: Create and activate a virtual environment (recommended)

python3 -m venv venv
source venv/bin/activate

Step 3: Install dependencies

pip install -r requirements.txt

Step 4: Run the project

python3 Heart_Disease.py

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📈 Results

• Model Accuracy: ≈ 85%
• Visualization: Confusion Matrix displaying predicted vs. actual results
• Evaluation Metric: Accuracy Score

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🧩 Ethical Reflection

This model uses publicly available, anonymized data. It is intended for educational and research purposes only, not for real medical diagnosis. Ethical AI means protecting privacy and avoiding misuse of health predictions.

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🌟 Impact

By integrating AI with healthcare, this project demonstrates how machine learning can:

• Assist in early risk detection
• Support preventive healthcare
• Advance SDG 3: Good Health and Well-being