Analysis and comparison for accurate prediction of innovative Bradycardia using linear discriminant analysis classifier and support vector machine.

Using Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers, the purpose of this work is to get a higher level of accuracy in predicting the presence of Innovative Bradycardia disease. Methodologies and Instruments for Research: The LDA classifier and the Support Vector Machine technology were utilised in order to make a prediction regarding the accuracy percent of the predicted Innovative Bradycardia disease. The sample size for this prediction was forty. When calculating G-power, the alpha (0.05) and power (80 percent) categories are taken into consideration independently of one another. The findings of the study indicated that when comparing the accuracy rates of the two study groups using SVM and LDA classifiers, there was a noteworthy disparity (p-value=0.001, p<0.05 in independent sample t-test). The LDA classifier had a staggering 93.5 percent success rate, in contrast to the Support Vector Machine technique, which only managed to achieve a success rate of 73.5 percent. In accordance with the findings, the utilisation of Linear Discriminant Analysis classifiers that have a wide range of seed values results in a significant improvement in the prediction of Innovative Bradycardia. [ABSTRACT FROM AUTHOR]