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Clinical Decision Support Systems for Predicting Patients Liable to Acquire Acute Myocardial Infarctions

  • Conference paper
  • First Online:
Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

Abstract

Acute myocardial infarction (AMI) is a major cause of death worldwide. There are around 0.8 million persons suffered from AMI annually in the US and the death rate reaches 27%. The risk factors of AMI were reported to include hypertension, family history, smoking habit, diabetes, serenity, obesity, cholesterol, alcoholism, coronary artery disease, etc. In this study, data acquired from a subset of the National Health Insurance Research Database (NHIRD) of Taiwan were used to develop the clinical decision support system (CDSS) for predicting AMI. Support vector machine integrated with genetic algorithm (IGS) was adopted to design the AMI prediction models. Data of 6087 AMI patients and 6087 non-AMI patients, each includes 50 features, were acquired for designing the predictive models. Tenfold cross validation and three objective functions were used for obtaining the optimal model with best prediction performance during training. The experimental results show that the CDSSs reach a prediction performance with accuracy, sensitivity, specificity, and area under ROC curve (AUC) of 81.47–84.11%, 75.46–80.94%, 86.48–88.21%, and 0.8602–0.8935, respectively. The IGS algorithm and comorbidity-related features are promising in designing strong CDSS models for predicting patients who may acquire AMI in the near future.

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Acknowledgments

This study was partially supported by Ministry of Science and Technology, Taiwan (MOST 109-2410-H-166-001) and Central Taiwan University of Science and Technology, Taichung, Taiwan (Grant No. CTU108-P-019). Fu-Hsing Wu, Hsuan-Hung Lin, and Po-Chou Chan contributed equally to this work. Correspondence should be addressed to Yung-Fu Chen or Chih-Sheng Lin.

Author information

Authors and Affiliations

  1. Department of Radiology, BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, 210019, Jiangsu, China

    Yung-Fu Chen & Chih-Sheng Lin

  2. Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Fu-Hsing Wu, Hsuan-Hung Lin, Po-Chou Chan & Chien-Ming Tseng

  3. Department of Dental Technology and Materials Science, Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Yung-Fu Chen

  4. Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Chih-Sheng Lin

Authors
  1. Fu-Hsing Wu
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  2. Hsuan-Hung Lin
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  3. Po-Chou Chan
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  4. Chien-Ming Tseng
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  5. Yung-Fu Chen
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  6. Chih-Sheng Lin
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Corresponding authors

Correspondence to Yung-Fu Chen or Chih-Sheng Lin .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Wu, FH., Lin, HH., Chan, PC., Tseng, CM., Chen, YF., Lin, CS. (2020). Clinical Decision Support Systems for Predicting Patients Liable to Acquire Acute Myocardial Infarctions. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_54

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

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