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Establishment and Analysis of a Combined Diagnostic Model of Acute Myocardial Infarction Based on Random Forests and Artificial Neural Networks

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Medical Imaging and Computer-Aided Diagnosis (MICAD 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 810))

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Abstract

Acute myocardial infarction is a serious disease worldwide that kills approximately 8.5 million patients each year. It can occur in multiple age groups and, despite the more diverse diagnostic techniques available, it has a number of limitations. Therefore, a diagnostic model based on gene biomarkers should be developed to assist existing diagnostic methods and improve the efficiency of diagnosis. For this research, we applied three datasets, one for screening DEGs and the other two for validation. We selected the DEGs of AMI from the first dataset and used a random forest classifier to identify key genes, including TREM-like transcript 2 (TREML2), interleukin-1 receptor type 2, CSF3R, HMGB2, nuclear factor interleukin 3 regulated, granzyme K (GZMK), MXD1, KIAA1324, NTNG2, and LOC440737. Among these genes, TREML2, HMGB2, GZMK, MXD1, KIAA1324, NTNG2, and LOC440737 have never been associated with AMI. Next, we successfully used an artificial neural network to construct a new model to diagnose AMI and verified the diagnostic effect of the model using the two validation datasets.

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Acknowledgement

This study was supported by Shanxi Province Graduate Education Innovation Project (2022Y37), and Provincial Science and Technology Grant of Shanxi Province (20210302124588).

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Authors and Affiliations

  1. Changzhi Medical College, Changzhi, Shanxi, China

    Zhenrun Zhan, Xiaodan Bi, Jinpeng Yang, Xu Tang & Tingting Zhao

  2. Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China

    Zhenrun Zhan, Xiaodan Bi, Jinpeng Yang, Xu Tang & Tingting Zhao

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  1. Zhenrun Zhan
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  2. Xiaodan Bi
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  3. Jinpeng Yang
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  4. Xu Tang
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  5. Tingting Zhao
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Corresponding author

Correspondence to Tingting Zhao .

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Editors and Affiliations

  1. Department of Computer Sciences and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Ruidan Su

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yudong Zhang

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Han Liu

  4. Computational Medicine, University of Manchester, Manchester, UK

    Alejandro F Frangi

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Zhan, Z., Bi, X., Yang, J., Tang, X., Zhao, T. (2023). Establishment and Analysis of a Combined Diagnostic Model of Acute Myocardial Infarction Based on Random Forests and Artificial Neural Networks. In: Su, R., Zhang, Y., Liu, H., F Frangi, A. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2022. Lecture Notes in Electrical Engineering, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-16-6775-6_28

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  • DOI: https://doi.org/10.1007/978-981-16-6775-6_28

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