Theses (Master In Health Informatics & Analytics)

Cardiovascular disease (CVD) is a general term describe a group of patients with disorder of the heart and blood vessels. It is one of the deadliest diseases in the world. However, vast amount of data with patients’ information collect by health management, can act as input of machine learning (ML) models, which is useful in making prediction. This prediction help health professional make intervention earlier to reduce mortality rate from CVD. In this study, effective CVD prediction models are developed using ML models. Feature selection and oversampling are applied in this study to reduce the redundancy data and improve performance of machine learning models. The machine learning models are applied on 5 open-source CVD datasets. The results are present by dashboard visualization which help audience understand easily. Results have proved that predictive model can identify the risk factor of CVD, identify the machine learning model with highest accuracy. Keywords: Cardiovascular disease, machine learning, feature selection, dashboard visualization.