Anika Pallapothu

How can machine learning diagnose coronary artery disease using adult data from blood tests and electrocardiograms?

Started Feb. 28, 2022

Abstract or project description

More than 18 million adults have coronary artery disease, with there being around 360,900 deaths in 2019 in the United States. Coronary artery disease (CAD) occurs when a person’s body creates cholesterol-containing deposits in their coronary arteries and inflammation follows. This causes the major blood vessels that supply the heart to become diseased (atherosclerosis), which can cause heart attacks or strokes. Overall, the coronary arteries are a vital part of the heart, since they supply all of the oxygen, nutrients, and blood. With an excess of plaque in these arteries, the blood flow will decrease causing either chest pain or shortness of breath, the two main symptoms of CAD. Sadly, this disease can only be found after a few decades with the patient either suffering from a heart attack or a blockage. Right now, the main ways to diagnose CAD are through blood tests, exercise stress tests, or by using echocardiograms. But these examinations will be used only after the patient has shown any symptoms of the disease. On the other hand, my project will be able to diagnose coronary artery disease using 13 different parameters from blood test and electrocardiogram (ECG) results before it is too late so that the patient can take any necessary precautions to avert fatal outcomes. Links: https://www.verywellhealth.com/how-heart-disease-is-diagnosed-4163340 https://www.mayoclinic.org/diseases-conditions/coronary-artery-disease/diagnosis-treatment/drc-20350619 https://www.mayoclinic.org/diseases-conditions/coronary-artery-disease/symptoms-causes/syc-20350613 https://www.cdc.gov/heartdisease/facts.htm