Heart disease prediction model using tree-based method

Yanran Li; Yitong Liu; Jin Luo; Xiao Sun

doi:10.1117/12.2639449

17 May 2022 Heart disease prediction model using tree-based method

Yanran Li, Yitong Liu, Jin Luo, Xiao Sun

Proceedings Volume 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022); 1225935 (2022) https://doi.org/10.1117/12.2639449
Event: 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing, 2022, Kunming, China

Abstract

Heart disease has become a global trend illness, with high mortality and death toll. Based on the accumulated case data, data analysis can be used to predict potential heart disease patients more accurately and optimize the treatment. Comparing with other research, this study uses the most basic algorithm in the tree-based methods to predict the data. In the research, some exploratory analysis on the heart disease data set is done firstly. By analyzing the relationship between each variable, the symptom factors related to heart disease are obtained preliminarily. Then this study develops two heart disease prediction models using two kinds of tree methods and compares the error of the two models. It turns out that the random forest model fits better than the decision tree model which can predict the data much more accurate. This can help medical professionals predict heart disease status based on a patient's clinical data and improve patient treatment efficiency. Also, the study gives hints about the causes of the heart diseases, which will be useful in preventing these fatal illnesses.

Citation Download Citation

Yanran Li, Yitong Liu, Jin Luo, and Xiao Sun "Heart disease prediction model using tree-based method", Proc. SPIE 12259, 2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2022), 1225935 (17 May 2022); https://doi.org/10.1117/12.2639449

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