Innovative accuracy in early identification of ischaemic stroke using naive bayes classifier with support vector machine

The main objective of this study is to create a unique Naive Bayes (NB) classifier that can predict ischemic strokes better than cutting-edge Support Vector Machine (SVM) techniques. Twenty Support Vector Machines (SVMs) and twenty naïve Bayes classifiers are compared in this study. The G power technique was used to calculate the total sample size, with an allocation ratio of 1 and an alpha of 0.05. While the recommended method achieves 98% success rate, the support vector machine achieves 90%. The statistical analysis indicated that the specificity p-value was 0.010 and the accuracy p-value was 0.042. When it comes to brain stroke identification, the novel Naive Bayes classifier performs better than support vector machine classifiers.