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Predictive models for detecting patients more likely to develop acute myocardial infarctions

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Abstract

Acute myocardial infarction (AMI) is a major cause of death worldwide. In the USA, there are approximately 0.8 million persons suffering from AMI annually with a death rate of 27%. The risk factors of AMI include hypertension, family history, smoking habits, diabetes, serenity, obesity, cholesterol, alcoholism, coronary artery disease, and so forth. In this study, data acquired from a subset of the National Health Insurance Research Database (NHIRD) of Taiwan were used to develop a clinical decision support system (CDSS) to predict AMI. The integrated genetic algorithm and support vector machine (IGS) and deep neural network (DNN) were both applied to design the predictive models. A balanced dataset (6087 AMI patients and 6087 non-AMI patients) and an imbalanced dataset (6,087 AMI patients and 12,174 non-AMI patients) with each patient record including 74 features were retrieved to design the predictive models. Tenfold cross-validation was used to obtain the optimal model with best prediction performance during training. The experimental results showed that the CDSSs reached a prediction performance with accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 79.75–84.4%, 68.29–83.7%, 82.45–92.07%, and 0.8424–0.9089, respectively, for models designed based on the balanced dataset, as well as 81.86–86.27%, 52.65–81.22%, 84.29–96.47%, and 0.8503–0.9098, respectively, for models implemented based on the imbalanced dataset. The IGS and DNN algorithms and a combination of age, presence of related comorbidities, and other comorbidity-related features, including diagnosed age and annual physician visits of individual comorbidities, have been shown to be promising in designing strong predictive models in detecting patients who are more likely to develop AMI in the near future as well as for realizing mobile-health (m-Health) systems in managing their comorbidities to prevent occurrence of AMI events. Future work will focus on realizing an ensemble model by combining the model designed based on the long-term NHIRD dataset and the model based on the short-term EMR data and the real-time IoT sensor data, as well as implementing a transfer learning model by transferring the knowledge learned from the long-term model for training the short-term model, so that the predictive performance can be enhanced.

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Authors and Affiliations

  1. Department of Radiology, BenQ Medical Center, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210019, 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 & Kun-Min Chang

  3. Department of Artificial Intelligence and Health Management, Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Fu-Hsing Wu

  4. Department of Rehabilitation, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, 42055, Taiwan

    Huey-Jen Lai

  5. Department of Healthcare Administration, Central Taiwan University of Science and Technology, Taichung, 40601, Taiwan

    Huey-Jen Lai

  6. Department of Obstetrics and Gynecology, Feng Yuan Hospital, Ministry of Health and Welfare, Taichung, 42055, Taiwan

    Kun-Min Chang

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

    Yung-Fu Chen

  8. Department of Health Services Administration, China Medical University, Taichung, 40402, Taiwan

    Yung-Fu Chen

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

    Chih-Sheng Lin

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  1. Fu-Hsing Wu
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Correspondence to Yung-Fu Chen or Chih-Sheng Lin.

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This work was funded in part by Central Taiwan University of Science and Technology (Grant CTU108-P-019) and Ministry of Science and Technology of Taiwan (Grants MOST 107-2410-H-166-003 & MOST 105-2410-H-166-006). Fu-Hsing Wu, Huey-Jen Lai, and Hsuan-Hung Lin contributed equally to this work.

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Wu, FH., Lai, HJ., Lin, HH. et al. Predictive models for detecting patients more likely to develop acute myocardial infarctions. J Supercomput 78, 2043–2071 (2022). https://doi.org/10.1007/s11227-021-03916-z

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