Preliminary Experiment Results of Left Ventricular Remodelling Prediction Using Machine Learning Algorithms


  • D.N.F. Awang Iskandar Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • P.C. Lim Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • A. Said Faculty of Medicine and Health Sciences, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • N.H. Mohd Amin Cardiology Department, Sarawak Heart Centre, Jalan Expressway, 94300 Kota Samarahan, Sarawak, Malaysia.
  • A. Khan Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
  • H. Ujir Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.


Machine Learning, Left Ventricular Remodeling, Magnetic Resonance Imaging, Classification.


Left ventricular remodelling involves changes in the ventricular size, shape and function where abnormalities eventually lead to heart failure. Early prediction of left ventricular remodelling can help in enhancing clinical decision making in cardiac health management and reducing cardiovascular mortality. Although cardiac magnetic resonance imaging is increasingly being used in clinical assessment of cardiovascular diseases, there is scarce study on predicting the presence of left ventricular remodelling given the derived data from cardiac magnetic resonance images. Four parameters namely left ventricular end diastolic volume, left ventricular end systolic volume, ejection fraction and occurrence/absence of oedema are used for prediction. A preliminary experiment is conducted where multi-layer perceptron and support vector machine are trained with the parameters obtained from cardiac magnetic resonance images in predicting between patients with left ventricular remodelling or normal. The preliminary experimental results indicated that support vector machine model performed better than multi-layer perception.


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How to Cite

Awang Iskandar, D., Lim, P., Said, A., Mohd Amin, N., Khan, A., & Ujir, H. (2017). Preliminary Experiment Results of Left Ventricular Remodelling Prediction Using Machine Learning Algorithms. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-10), 119–124. Retrieved from