Machine learning models predict cardiovascular disease with high accuracy in a study using Kaggle data, suggesting effective diagnostic tools may reduce medical errors.
Key Points
Accuracy of machine learning algorithms reached 98.18% on training data, highlighting effective predictive capabilities for cardiovascular disease.
The Random Forest model showed 79.22% accuracy on testing data, outperforming other algorithms in the study.
Analysis followed the CRISP-DM framework, incorporating data visualization and cleansing techniques to prepare the dataset.
This research indicates machine learning's potential to enhance diagnostics in healthcare settings, particularly in resource-limited areas.