Abstract:
Coronary heart disease includes the formation of plaque around the walls of arteries
of heart. That causes the arteries to be tightened and restricts the flow of blood inside the
artery. The left ventricle is the main powerhouse that pumps and supply the blood to other
organs via arteries. The ejection fraction is the proportion of blood that leaves heart each
time it squeezes (contracts). American Heart Association describes the ejection fraction
threshold of left ventricle is 50 to 70 percent for normal and anything below 50 percent is
alarming. Ejection fraction can be measured by Echocardiography, MRI, CT scans,
Nuclear Medicine Scan and Cardiac Catheterization. However, these tests are time
demanding and might led to death due to wrong or delayed medication. Therefore, in
present study machine learning models were developed for the prediction of ejection
fraction of left ventricle for the diagnostics and economically personalized treatments. In
present study, patient cardiac records have been used to train a model and predicted the
ejection fraction by clinical dataset of 1343 which includes angiography and pathology
reports having clinical features like heart rate, age, Troponin-I, low density lipoproteins,
diabetes, systolic and diastolic blood pressures along with target variable having ejection
fraction classes of normal, mild, moderate, and severe patients. Several models like
Decision Trees, Gradient Boosted Trees, K-Nearest Neighbors, Artificial Neural
Networks, Naïve Bayes, Support Vector Machine have been developed. However, out of
these models, nonlinear support vector machines using polynomial kernel achieved 97
percent accuracy, which shows the robustness of the model, and that model can be used to
predict ejection fraction for quick and accurate diagnostics and personalized therapies for
coronary heart disease patients.
