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) Classifier in predicting Innovative Bradycardia disease diagnosis. Materials and Methods: There are 7,500 records in the dataset that was used for this investigation. 40 records are utilized in the tes...

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Bibliographic Details
Main Authors: Devisetty, Gowtham, Kumar, Neelam Sanjeev
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithms
Bradycardia
Cardiac arrhythmia
Classifiers
Confidence intervals
Decision analysis
Decision making
decision tree classifier
Decision trees
disease
innovative bradycardia
machine learning algorithms
Mental task performance
prediction
Statistical analysis
support vector machine
Support vector machines
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Summary:This study compares the Accuracy of Support Vector Machine (SVM) Classifier and Decision Tree (DT) Classifier in predicting Innovative Bradycardia disease diagnosis. Materials and Methods: There are 7,500 records in the dataset that was used for this investigation. 40 records are utilized in the test to get a 95% confidence level in Accuracy and a 1% margin of error. There are 12 qualities or features per record. Using Decision Tree and SVM, Innovative Bradycardia disease is detected. Results: According to the statistical analysis, the Accuracy of the Decision Tree Classifier was 92.62%, P
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202339909004