Computational Vision and Bio Inspired Computing pp 496-505 | Cite as
Identification of the Risk Factors of Type II Diabetic Data Based Support Vector Machine Classifiers upon Varied Kernel Functions
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Abstract
In the innovation in making a data driven decision model, the technological impacts across medical data processing has been increasing rapidly. Type II diabetes is one among the majorly increasing non-communicable disease across India. Advancements in the field of medicine its therapeutic procedures have to be considered in determining the risk factor related to disease prediction and its major cause. This research work, focus towards the applicability of Support Vector Machine (SVM) classifiers for predicting the risk related to type II diabetes. The model involves the deployment of various kernel functions upon the building of SVM models. The experimental results shows that data classification using SVM upon varied kernel function has improvement over the accuracy with 86.65%, precision 76.21% and recall with 81.11% respectively. Among the kernels experimented, polynomial kernel performed better than the other kernels with increased correlation results. The variation in the kernel with model enhancements can be deployed for risk factor prediction in type II diabetes.
Keywords
Data classification Decision model Risk factors Kernel functionsReferences
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