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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Thiruvarasu Vasantha Priya, Maran Jeyanthiran
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Computer Science & IT Engineering

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

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Scalability and performance of decision tree for cardiovascular disease prediction Admassu Assegie, Tsehay; Kumar Napa, Komal; Thulasi, Thiyagu; Kalyan Kumar, Angati; Thiruvarasu Vasantha Priya, Maran Jeyanthiran; Dhamodaran, Vigneswari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2540-2545

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.
Co-Authors Admassu Assegie, Tsehay Dhamodaran, Vigneswari Kalyan Kumar, Angati Kumar Napa, Komal Thulasi, Thiyagu