Exploring the Potential of Supervised Learning Methods for the Investigation and Identification of Cardiovascular Diseases Using Machine Learning
Keywords:
Cardiovascular Health Evaluation, Classifiers, Predictive Modeling, Diagnostic Evaluation metrics, Risk Prediction.Abstract
Cardiovascular-disease continues to be a worldwide concern, especially impacting countries that are economically disadvantaged. The vast data sets that are generated by the healthcare business may now be processed by machine learning algorithms. This research uses Grid-Search CV to tune parameters and compare six classification techniques for selecting the most accurate prediction model. It is innovative in applying these algorithms to assess patients' risk factors based on medical information. Study results identified major risk factors of cardiovascular disease. The machine proceeds to the next classification step, if an individual's F1-Score exceeds 91.3%. Within the scope of the study, a number of different machine learning approaches were evaluated for classification tasks. The accuracy of logistic regression was 0.90%, the accuracy of support vector classifier was 0.91%, the accuracy of k-nearest was 0.92%, the accuracy of Gaussian Naive Baye's was 0.91%, and the accuracy of decision tree classifier was 0.89%. Finally, the Random Forest Classifier had the highest accuracy of all the approaches that were investigated, coming in at 0.96%. These findings demonstrate that varied approaches are used by machine learning systems when it comes to classification problems.
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