A heart failure staging model based on machine learning classification algorithms

doi:10.3969/j.issn.2095-4344.2014.49.012

Abstract

Abstract:

BACKGROUND: Early detection and accurate staging diagnosis of heart failure are the basis of good clinical therapy efficacy. Due to lack of simple and effective staging model for the diagnosis of heart failure, it is difficult to diagnose heart failure in clinics, leading to poor control of heart failure.

OBJECTIVE: To establish the disease staging model based on Adaboost and SVM for heart failure, and improve the accuracy of diagnosis and staging of heart failure.

METHODS: A total of 194 cases were rolled into this study, including heart failure patients and healthy physical examination persons. According to the stage standards formulated by American College of Cardiology and American Heart Association, specific clinical feature parameters closely related to heart failure were collected and selected. Based on clinical diagnosis results and using Adaboost model and SVM model, we trained the models for heart failure diagnosis and staging, thus obtaining diagnosis model.

RESULTS AND CONCLUSION: The parameters included stroke volume, cardiac output, left ventricular ejection fraction, left atrial diameter, left ventricular internal diameter at end-systole, N-terminal pro-brain natriuretic peptide and heart rate variability. As for the Adaboost model, its sensitivity and specificity was 100% and 94.4%, respectively. At the same time the SVM model had good sensitivity and specificity, 86.5% and 89.4% respectively. Adaboost classification model can be accurate in the diagnosis of heart failure symptoms, the accuracy reached 89.36%. On the basis of the diagnosis of heart failure, the SVM classification model is effective in staging the severity of heart failure, staging accuracy for staging B and C was 86.49% and 81.48%, respectively. The findings indicate that, combining Adaboost and SVM machine learning models could provide an accurate diagnosis and staging model for heart failure.

中国组织工程研究杂志出版内容重点：肾移植；肝移植；移植；心脏移植；组织移植；皮肤移植；皮瓣移植；血管移植；器官移植；组织工程

全文链接：

Key words: heart failure, diagnosis

CLC Number:

R318

Su Feng, Zhang Shao-heng, Chen Nan-nan, Wang Jia-hong, Yao Jian-hua, Tang Jing-hui, Wu Wen-mei, Chen De . A heart failure staging model based on machine learning classification algorithms[J]. Chinese Journal of Tissue Engineering Research, 2014, 18(49): 7938-7942.

Figures/Tables 2

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