Key result
Machine learning classifiers enable the development of binary models for predicting cardiovascular attacks.
Why the study?
Cardiac attacks are a leading cause of mortality worldwide, making timely and accurate prediction a formidable challenge in clinical data analysis.
Machine learning algorithms can be utilized to create prediction models for cardiovascular attacks to improve detection accuracy.
ML models may support CV attack prediction but should not yet change practice; leaves open need for prospective validation.
Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. Machine Learning (ML) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing ML techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions.
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Durai et al. (2024) studied Cardiovascular attacks. Machine Learning classifiers (NB, KNN, SVM, RF, DT, LR) was evaluated on Cardiovascular attack prediction accuracy. Machine learning classifiers, including Naïve Bayes, KNN, SVM, Random Forest, Decision Tree, and Logistic Regression, were utilized to develop a binary classification model for predicting cardiovascular attacks.
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