Kernel Parameter Tuning to Tweak the Performance of Classifiers for Identification of Heart Diseases

Annu Dhankhar, Sapna Juneja, Abhinav Juneja and Vikram Bali
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Annu Dhankhar: B. M. Institute of Engineering and Technology, Sonepat, India
Sapna Juneja: IMS Engineering College, Ghaziabad, India
Abhinav Juneja: KIET Group of Institutions, Delhi NCR, Ghaziabad, India
Vikram Bali: JSS Academy of Technical Education, Noida, India

International Journal of E-Health and Medical Communications (IJEHMC), 2021, vol. 12, issue 4, 1-16

Abstract: Medical data analysis is being recognized as a field of enormous research possibilities due to the fact there is a huge amount of data available and prediction in initial stage may save patient lives with timely intervention. With machine learning, a particular algorithm may be created through which any disease may be predicted well in advance on the basis of its feature sets or its symptoms can be detected. With respect to this research work, heart disease will be predicted with support vector machine that falls under the category of supervised machine learning algorithm. The main idea of this study is to focus on the significance of parameter tuning to elevate the performance of classifier. The results achieved were then compared with normal classifier SVM before tuning the parameters. Results depict that the hyperparameters tuning enhances the performance of the model. Finally, results were calculated by using various validation metrics.

Date: 2021
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Citations: View citations in EconPapers (3)

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