Prediction of Bradycardia using Decision Tree Algorithm and Comparing the Accuracy with Support Vector Machine

This study compares the Accuracy of Support Vector Machine (SVM) Classifier and Decision Tree (DT) in predicting Innovative Bradycardia disease diagnosis. Materials Methods: There are 7,500 records dataset that was used for this investigation. 40 utilized test to get a 95% confidence level 1% margin error. 12 qualities or features per record. Using SVM, is detected. Results: According statistical analysis, 92.62%, P<0.05, SVM 87.5%, P<0.05. The p value calculated as 0.001 (p<0.05, independent sample t-test indicating statistically significant difference accuracy rates between two algorithms (SVM DT). Conclusion: In prediction task, (92.5%) exhibited improvement over (87.5%), demonstrated by findings present study.