Heart Disease Prediction Using Machine Learning
Heart Disease Prediction Using Machine Learning
In this project, I applied machine learning models to predict the likelihood of heart disease based on a dataset of health metrics. It was the capstone project for my MSc in Data Science & Artificial Intelligence.
Objective
To build a predictive model that can support early diagnosis and personalized healthcare using real-world health data.
Tools & Technologies
- Languages: Python
- Libraries: pandas, NumPy, scikit-learn, seaborn, matplotlib
- Models Used: Logistic Regression, Random Forest, XGBoost, SVM, KNN, and MLP
- Evaluation Metrics: Accuracy, F1-score, Confusion Matrix, ROC Curve
Outcomes
- Random Forest and XGBoost were the top performers
- Provided visual insights into feature importance and data distribution
- Demonstrated effective preprocessing, model tuning, and evaluation
GitHub Repo: View on GitHub
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