AI-Based Prediction of Myocardial Infarction in Patients Using Various Algorithms
Keywords:
Logistic Regression, Myocardial Infarction, Support Vector Machine, Prediction, K-Nearest Neighbours, Artificial Intelligence, Clinical Decision Making Random Forest ClassifierAbstract
Myocardial Infarction (MI) is among the primary causes of mortality worldwide and early detection of this condition can improve patient outcomes. Artificial Intelligence (AI) have shown promise in predicting Myocardial Infarction in patients, but the optimal algorithm for this task is not yet clear. This study assessed the efficacy of four ML algorithms - K-nearest neighbours (KNN), Logistic regression, Support Vector Machine (SVM), and random forest analysis - in predicting MI in patients. This study includes Myocardial Infarction dataset of 303 patients with details of medical history, demographic info, as well as clinical constraints. The data pre-processing was done with missing values, removing outliers and normalizing the data. In addition, feature selection approaches identify the most relevant predictors of myocardial infarction. The accuracy metrics are determined by evaluating the training performance of the four algorithms on a practice set. When the results are compared, Logistic Regression outplays the others with an overall accuracy of 81.32%. However, K-nearest neighbors, SVM, and Random Forest had accuracy rates of 65.93%, 54.95%, and 81.32%, respectively. Thus, according to our research findings, Logistic Regression is the optimal algorithm for predicting MI in patients. It is a straightforward, interpretable, and efficient technique that can be used in clinical decision-making. Our findings provide essential data about the use of machine learning algorithms to predict myocardial infarction and can help guide future studies in this area.
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