The primary objective of implementing machine learning (ML) in the detection of cardiovascular disease is to enhance early and accurate identification of individuals at risk, enabling timely intervention and personalized healthcare. Through the utilization of advanced ML algorithms on diverse patient data, including clinical records, medical imaging, and genetic information, we aim to develop a robust and efficient system that can predict and classify cardiovascular diseases with high sensitivity and specificity. This implementation seeks to contribute to the improvement of patient outcomes by facilitating proactive healthcare strategies, optimizing resource allocation, and ultimately reducing the burden of cardiovascular diseases on both individuals and healthcare systems.
- Separating features (X) and target variable (y).
- Standardizing the data using
StandardScaler.
- Training the logistic regression model.
- Training the random forest model.
- Training the support vector machine model.
- Evaluating the models on the test set.
- Calculating and printing the accuracy scores for each model.
- Creating histograms for analyzing each model's working.
- Printing classification reports for each model.
- Determining the best model based on accuracy, generalizability, interpretability, computational cost, robustness, and calibration.
- Testing the models on a new dataset.
- Creating histograms for positive and negative predictions.
- Showing binary predictions (1 or 0) for each model.
- Preprocessing input data.
- Making real-time predictions using the trained Random Forest model.