Scalability and performance of decision tree for cardiovascular disease prediction

Tsehay Admassu Assegie, Komal Kumar Napa, Thiyagu Thulasi, Angati Kalyan Kumar, Maran Jeyanthiran Thiruvarasu Vasantha Priya, Vigneswari Dhamodaran

Abstract


As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the performance of a decision tree for predicting cardiovascular disease. The performance evaluation was carried out by employing a confusion matrix, cross-validation score, model complexity, and training score for varying sizes of training samples. The experiment depicted that, the decision tree model was 88.8% accurate in predicting the presence or absence of cardiovascular disease. Therefore, the implementation of the decision tree is beneficial for the prediction and early detection of heart disease events in patients.

Keywords


Automated diagnostics; Computational model; Machine learning; Scalability in machine learning

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DOI: http://doi.org/10.11591/ijai.v13.i3.pp2540-2545

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IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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