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Machine learning algorithm can provide assistance for the diagnosis of non-ST-segment elevation myocardial infarction
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  • Published on:
    Utility of ML algorithms: Are we predicting the outcome or finding the risk factors.
    • Thenmozhi Mani, Sr.Demonstrator Christian Medical College and hospital, Vellore, India
    • Other Contributors:
      • Marimuthu S, Associate Research Officer
      • Jeyaseelan L, Professor

    In the diagnosis of ST segment elevation MI, Qin et al (2021) had published an article which wherein the study was meticulously planned, analysed and interpreted in a comprehensive manner (1). The authors have used three ML algorithms to screen variables for prediction and evaluated six algorithms to select the best one that addressed the research question. As stated above the “aim was to use the constructed machine learning (ML) models as auxiliary diagnostic tools to improve the diagnostic accuracy of non-ST-elevation myocardial infarction (NSTEMI)”. Towards the end of a robust analysis, they were able to suggest an ML algorithm XGBoost as the best when compared to other algorithms and Logistic regression model as well.
    In the process of addressing the above aim, they have compiled data retrospectively with a total of 2878 patients from January 2017 to December 2019. Of them 1409 patients were diagnosed with NSTEMI and 1469 patients were diagnosed with unstable angina pectoris. Thus the percentage of NSTEMI in the study was 48.9%. Does it reflect the hospital prevalence of NSTEMI? That is, if 100 patients visit the Cardiology department, what percent of them will have NSTEMI? Supposing that if this is 10%, then would the above identified variables of importance be the same? The probability of a patient having NSTEMI be the same? The ML algorithms are expected to work very well with 50% probability of disease. By not defining the sampling method, the process of ba...

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    Conflict of Interest:
    None declared.