Prediction of Coronary Heart Disease Using Machine Learning
The Project
Background: Coronary Heart Disease (CHD) is the reduction of blood flow to the heart muscle due to blockage in the coronary arteries and is a leading cause of death in the USA. As such, successful CHD predictions will save lives. Machine Learning has been successfully used to predict weather, the stock market, etc. Hypothesis: Machine Learning approaches can be used to predict 10-year CHD risks in patients. Methods: Using Linear Discriminant Analysis in Python coding, I trained a Machine Learning model with a Framingham heart study dataset of more than 3,658 patients. Next, I predicted 10-year CHD risk in 37 patients. Results: The accuracy of the model turned out to be near 89%. In the test population, the model successfully predicted the 10-year CHD risk in the patients. Conclusion: Successful predictions of CHD using the Machine Learning approach will prevent the disease in patients with a high cardiovascular risk.
About the team
Team members
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