Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease

LU Wen-ting; YAO Yuan; XIONG Jing; LIU Xiang-ping; LI Shuang-qing

doi:10.16766/j.cnki.issn.1674-4152.002825

Volume 21 Issue 1

Jan. 2023

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Article Navigation > Chinese Journal of General Practice > 2023 > 21(1): 112-117

LU Wen-ting, YAO Yuan, XIONG Jing, LIU Xiang-ping, LI Shuang-qing. Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease[J]. Chinese Journal of General Practice, 2023, 21(1): 112-117. doi: 10.16766/j.cnki.issn.1674-4152.002825

Citation:

LU Wen-ting, YAO Yuan, XIONG Jing, LIU Xiang-ping, LI Shuang-qing. Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease[J]. Chinese Journal of General Practice, 2023, 21(1): 112-117. doi: 10.16766/j.cnki.issn.1674-4152.002825

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Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease

doi: 10.16766/j.cnki.issn.1674-4152.002825

1.
General Practice Medical Center, West China Hospital, Chengdu, Sichuan 610044, China

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2021YFS0014

2017SZYZF0002

Received Date: 2022-02-03
Available Online: 2023-04-07

Abstract

Abstract

Based on the survey, cardiovascular disease mortality ranks first in the total mortality rate of urban and rural residents in China. The incidence of cardiovascular diseases is still increasing. In the past decade, China has effectively promoted the construction of cardiovascular health. The state has called for shifting the main battlefield of cardiovascular disease from hospitals to communities. Therefore, improving the quality of primary care services are necessary to meet the growing health demand of the people. With the advent of the digital information age, machine learning is widely used in image recognition, speech recognition and natural language processing. Artificial intelligence (AI) is widely used in e-commerce, home, logistics and transportation, however, its impact on medical care has just begun. With the improvement of medical data availability and the rapid development of big data analysis methods, the successful application of artificial intelligence in medical field becomes possible. Under the guidance of relevant clinical problems, powerful AI technology can extract the clinical information hidden in massive data and then assist doctors in clinical decision-making. In recent years, with the national and social attention to primary medical care and the development of Internet information technology, the application of machine learning technology in the diagnosis and prediction of cardiovascular diseases has become a hot topic. Machine learning is gradually changing the way doctors diagnose diseases and their clinical decision-making, but the diagnosis and decision-making of each cardiovascular disease requires a certain degree of analysis with regard to disease and statistics, and the selection of the optimal machine learning algorithm can better solve clinical problems. By comparing the area under the curve, sensitivity, specificity, accuracy, F1 value, C statistical value and other quantitative indicators of the auxiliary diagnosis model of cardiovascular diseases in recent 5 years, this paper evaluates the advantages of machine learning under different disease classifications, systematically summarises the application of different AI methods in the diagnosis and prediction of cardiovascular diseases and evaluates related auxiliary diagnosis models.
- Artificial intelligence,
- Machine learning,
- Cardiovascular disease,
- Auxiliary diagnosis

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References(38)

References

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Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease

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