Abstract: Heart disease, also known as cardiovascular disease, is a general term for a variety of conditions that affect the heart and blood vessels. It is a chronic disease that can lead to serious events including heart attack and death. Heart disease is one of the leading causes of death in Canada and worldwide. The most common form of heart disease is Coronary Artery Disease (CAD) caused by atherosclerosis. Some heart attacks cause very little damage to the heart muscle and the heart can still pump strongly. Some heart attacks are larger and the muscle damage causes a weak heart. There are several heart tests that measure the strength of the heart such as an echocardiogram (an ultrasound of the heart that looks at the pumping strength of the heart and how the heart valves work), nuclear scans such as a MUGA scan, or a ventriculogram which is commonly done during an angiogram. After many lab tests and investigations data has been tabulated and ready to be analysed. Any acquired/ given data can be analysed and conclusions drawn accordingly. The acquired or given data usually exists in its crude or raw state. In our assignment, the acquired data consists of many physiological parameters which directly or indirectly lead to this disease. Data pre-processing helps to format the data into useful form by removing redundancy and noise, eliminating missing and non-numerical values, and also by normalization. Data analysis and visualization are carried out to improve the statistical analysis of given data. Logistic regression is carried out on the data since it contains lot of columns with categorical values. Accuracy, precision, and f1 score of the model have been measured. Various conclusions can be drawn from this interdependent data set and can be stored as historical data for future analysis.

Keywords: Coronary Artery Disease (CAD), Machine Learning, Data pre-processing, Logistic regression, accuracy, precision, and f1 score, atherosclerosis, physiological parameters, echocardiogram, MUGA and ventriculogram

PDF | DOI: 10.17148/IJARCCE.2020.9129

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