Risk stratification of patients for development of cardiac diseases using knowledge transferPapič, Aleš (Avtor) Bosnić, Zoran (Mentor) risk stratificationcardiovascular diseasesmachine learningknowledge transfersemi-supervised learningactive learningfuzzy learningsupervised clusteringpartially labeled examplesThis thesis addresses the risk stratification of patients for development of cardiac diseases using machine learning methods. Our approaches modify existing methodologies, such as semi-supervised learning, active learning, fuzzy learning and supervised clustering. Using these methods we perform knowledge transfer on partially labeled data. We use the posterior class probability and local modeling of prediction error to strategically select training examples. Evaluation is performed on public heart disease data set and on data from peripheral arterial disease survival study. During the evaluation process, we use different ratios of labeled examples. The results show that our approaches increase the inductive performance compared to learning algorithms trained exclusively on labeled data.20192019-09-11 08:45:03Magistrsko delo/naloga110024VisID: 22582COBISS_ID: 1538330051sl