The Classification of Heart Disease Based on Artificial Network

Yang Sun

doi:10.47297/taposatWSP2633-456929.20220303

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The Classification of Heart Disease Based on Artificial Network

Volume 3, Issue 3

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The Classification of Heart Disease Based on Artificial Network
The Classification of Heart Disease Based on Artificial Network
Changchun University of Technology,Jilin,Changchun,P.R. China,130000
Vol. 3, Issue 3, Pages: 134-142(2022)
Published： 2022 ，
DOI：10.47297/taposatWSP2633-456929.20220303
Accepted：

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YANG SUN. (2022). The Classification of Heart Disease Based on Artificial Network. Theory and Practice of Science and Technology, 3(3), 134-142..
DOI：

YANG SUN. (2022). The Classification of Heart Disease Based on Artificial Network. Theory and Practice of Science and Technology, 3(3), 134-142.. DOI： 10.47297/taposatWSP2633-456929.20220303.

摘要

Abstract

Heart disease is a chronic disease which is not infectious but has high mortality in clinic. It is difficult to be accurately analyzed by traditional medical decision-making and diagnosis methods. With the emergence of a large number of clinical diagnosis

treatment and examination reports and network electronic medical record data

information technology provides a large number of data basis for interventional medical diagnosis and auxiliary medical pathological diagnosis. Based on plenty of data for clinical diagnosis and treatment for effective extraction and processing

machine learning algorithms can make accurate diagnosis for heart diseases

predict the probability of disease disease cases and patients

and combined with relevant professional knowledge

in the field of balancing potential data analysis and processing

in order to get better disease diagnosis

improve disease prevention

diagnosis and research status.

关键词

Keywords

Heart diseaseElectronic medical recordsMedical pathological diagnosisAuxiliary medical diagnosisMachine learning

references

Tripathy R.K., Sharma L.N., Dandapat S. (2014) A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification. Healthcare Technology Letters, 1: 98-103.[2] Wongsathan R., Pothong P. (2016) Heart Disease Classification Using Artificial Neural Networks. Heart Disease Research and Analysis, 8: 96-99.[3] Ansari H.F., Namdeo V. (2019) An efficient SKNN based approach for heart disease classification. International Journal of Advanced Technology and Engineering Exploration, 6: 101-06. [4] Khened M., Alex V., Krishnamurthi G. (2018) Fully Convolutional Multi-scale Residual DenseNets for Cardiac Segmentation and Automated Cardiac Diagnosis using Ensemble of Classifiers. Medical Image Analysis, 51: 21-45. [5] Chang Kai. (2014) Comparison and Analysis of Data Mining Classification Algorithms Based on Neural Network. https://wenku.baidu.com/view/6af62c21be23482fb4da4caf.html.[6] Song B. Y. (2018) Biventricular automatic segmentation and heart disease classification for cardiac MRI based on deep learning. http://cdmd.cnki.com.cn/Article/CDMD-10701-1019004362.htm.[7] Zhuang Qiaohui. (2019) Research and application of Improved random forest Algorithm. http://cdmd.cnki.com.cn/Article/CDMD-10385-1019625879.htm.[8] Li Xiaoqian. (2019) Research on heart disease prediction method based on convolutional neural network. https://www.cnki.com.cn/Article/CJFDTotal-HLDZ201901016.htm.[9] Otoom A. F., Abdallah E. E., Kilani Y., et al. (2015) Effective diagnosis and monitoring of heart disease. International Journal of Software Engineering and Its Applications, 9: 143-56.

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