Abstract—Chest pain is sudden, its pathological causes are
complex and various, fatal or non-fatal so that improving the
diagnostic accuracy is extremely important in the emergency
system of prehospital and hospitals. Therefore, we propose a
method of introducing a decision tree, support vector machine,
and KNN algorithm in machine learning into the auxiliary
diagnosis of chest pain. First select the algorithm with better
performance among decision tree, support vector machine, and
KNN algorithm; Then compare the classification performance
of the CART algorithm, the support vector machine using the
Gaussian kernel function, and the K nearest neighbor algorithm
using the Euclidean distance to select the best; Finally, through
the analysis of the experimental results, the support vector
machine algorithm with Gaussian kernel function is obtained.
Its detection time and diagnosis accuracy rate are the best
among the three algorithms, which can assist medical staff in
the emergency system to carry out targeted chest pain
diagnosis.
Index Terms—Chest pain diagnosis, machine learning, decision tree, support vector machine, KNN, classification accuracy, comparison selection.
Wen Gao, Rong Yu, Zhuang Ma, and MD MASUM are with the School of Information and Electronic Engineering, Shandong Technology and Business University, China (e-mail: wengao@sdtbu.edu.cn, yurong82@qq.com, mazhuang550043@163.com, md.masum.bd@outlook.com).
Zhaolei Yu is with Yan Tai En Bang Electronic Technology Co., Ltd., China (e-mail: 2606442593@qq.com).
Cite: Wen Gao, Rong Yu, Zhaolei Yu, Zhuang Ma, and Md Masum, "Auxiliary Diagnosis Method of Chest Pain Based on Machine Learning," International Journal of Engineering and Technology vol. 14, no. 4, pp. 79-83, 2022.
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