MLMI: A Machine Learning Model for Estimating Risk of Myocardial Infarction

Abstract

Cardiovascular diseases (CVD) are a global threat of high morbidity and mortality. Myocardial infarction (MI) due to coronary vessel malfunctions is one of the leading causes of mortality due to CVD. Interestingly, all CVD patients do not develop MI, and vice versa. Clinically, thus, it is a gray area. Therefore, an appropriate MI risk scoring (MIRS) tool could be useful to identify the high-risk (HR) population suffering from CVD. This research paper presents a hybrid machine learning (ML) model (MLMI) to identify MI risk where a) clustering of the CVD population with the help of the Gaussian mixture model (GMM) is used to identify the HR and not high-risk (NHR) groups, b) feature engineering of the members in both the HR and NHR populations using regression method that estimates the coefficient of determination (R2) to explore significant features to create the model by c) leveraging the R2 values > 0.7 as the key features of the input dataset to a d) Feed-forward neural network (FFNN) for scoring the risk on a set of synthetic patient data, created by three experienced medical doctors. The myocardial infarction risk scores (MIRS) would assist users in prioritizing the patients needing monitoring and treatment. Finally, the MIRS values are validated by another group of three medical doctors to curb the research bias. The sensitivity, specificity, precision, F1 scores, and accuracy of the MLMI model are computed to measure its efficiency. With limited input data, the proposed model shows an average accuracy, and precision of 77.33% each, while sensitivity and F1 score are 100% and 88%, respectively.

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“…Machine learning has become increasingly popular in recent years for predicting and stratifying diseases that involve multiple factors 24 . By analyzing multiple variables, machine learning can identify important combinations for diagnosing and prognosing diseases 25 , and can detect nonlinear relationships between them 26 . This makes it a flexible tool for handling various types of variables and extracting hidden patterns that may not be visible to clinicians 27 .…”

“…CNNs have been successfully applied in various domains, including object detection, facial recognition, and medical image analysis. CNNs are powerful deep-learning algorithms that can handle complex image data with high accuracy 26 , 38 .…”