Cardiovascular Disease Prediction

A project leveraging Machine Learning algorithms to predict and prevent Cardiovascular Diseases. Utilizing vast datasets of patient health records.

Upon comparing other models, the Histogram Gradient Bossting Classifier had superior performance. As a result, it was selected as the final model and fine-tuned to achieve a high level of accuracy. Made a prediction based on new data at last, and it turned out to be accurate.

Libraries

• Numpy
• Pandas
• Matplotlib
• Seaborn
• Imbalanced-Learn
• Scikit-Learn
• Joblib

Models

• Logistic Regression
• Support Vector Classifier
• Decision Tree Classifier
• Histgram Gradient Boosting Classifier

Metrics (HistGradient Boosting Model)

• Accuracy: 99.00%
• Precision: 99.34%
• Recall: 98.77%
• F1-Score: 99.07%
• ROC AUC: 1.00%