Heart Attack Prediction using Machine Learning

Project Overview

This project predicts the possibility of a heart attack using machine learning techniques based on medical and health-related parameters.

The model is trained using a dataset containing patient information such as age, cholesterol level, blood pressure, heart rate, and other medical attributes.

This project was developed as part of my internship learning program.

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Objective

The main objective of this project is to analyze patient health data and predict the risk of heart attack using machine learning algorithms.

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Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook

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Dataset Features

Some of the features used in the dataset include:

• Age
• Gender
• Cholesterol
• Blood Pressure
• Heart Rate
• Chest Pain Type
• Fasting Blood Sugar
• Exercise Induced Angina
• ECG Results

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Machine Learning Algorithm

The project uses:

• Logistic Regression / Random Forest / Decision Tree (Add the algorithm you actually used)

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Project Workflow

1. Data Collection
2. Data Preprocessing
3. Exploratory Data Analysis
4. Feature Selection
5. Model Training
6. Model Testing
7. Prediction
8. Accuracy Evaluation

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Results

The machine learning model predicts whether a person is at risk of a heart attack based on medical attributes.

Model Accuracy: XX% (Add your actual accuracy score)

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Output Visualizations

The project includes:

• Correlation Heatmap
• Data Distribution Graphs
• Prediction Results
• Accuracy Metrics

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Future Improvements

• Improve model accuracy
• Deploy using Flask or Streamlit
• Add real-time prediction interface
• Use advanced ML algorithms

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Conclusion

This project demonstrates how machine learning can help in early prediction of heart attack risk and support healthcare decision-making.

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Author

Jhansi B.Tech Student | Aspiring Data Analyst