Browsing by Author "Murugan, Kalavaani"
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- ItemClassification of Selected Cardiac Abnormalities through Machine Learning.(Stellenbosch : Stellenbosch University, 2022-04) Murugan, Kalavaani; Erfort, G; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.ENGLISH SUMMARY: Cardiovascular diseases contribute to a large number of deaths worldwide per year. From an engineering perspective, an opportune point of intervention is the examination phase of a patient where the equipment and supporting software is concerned. This study aims to develop a prototype supervised machine learning algorithm that can be used as a diagnostic tool in medical practise. Four hundred and six (406) Echocardiography examinations were col- lected containing six (6) different cardiac abnormalities associated with the left ventricle and aortic valve. Data was considerably insufficient thus augmenta- tion techniques were required to generate synthetic samples. Image processing techniques and various calculations were used to derive measurements and fea- tures to be suitable input for the machine learning models. Random Forest and Neural Network models with a variety of dimensions were developed and trained in 3 different tests. The first 2 tests investigated the value of engi- neering (measurement-derived) and medical (patient information) features to model outputs. Test 3 investigated the effect of various training set si'.es. Both models were better informed by medical features than those extracted geometrically or calculated. This was found due to the effect of noise distort- ing measurements extracted for features. Models also performed better on the largest training set si'.e (90% of data). All models were evaluated by selected performance metrics and/or learning curves (where applicable). The most suitable model selected was a Random Forest instance, as Neural Networks were prone to overfitting training data. These results were not true refiections of either model's capabilities due to the underlying data representativeness issue.