Stroke Risk Prediction through Non-linear Support Vector Classification Models

Sabibullah Mohamed Hanifa, Kasmir Raja S.V

Abstract


The aim of this study is to find the possible risk of Cerebro Vascular Accident (CVA) or Stroke by subjecting the risk factors to
Support Vector Machines (SVM).The prediction of the attack of the disease is highly dependent on the quantification of risks contributed by
each factor. Therefore an assessment of relative intensity of risk contributed by the factors is imperative for early prediction and preventable
measures. The classification accuracies are achieved through the efficient kernel functions of Radial Basis Function (RBF=98%) and Polynomial
(Poly=92%) and finally these results are compared with benchmarking evaluation methods like classification accuracy, sensitivity, specificity
and confusion matrix. The proposed stroke risk prediction models are obtained with satisfactory accuracy and it would be promising models in
the classification of stroke risk prediction process.

 

Keywords: Support Vector Machines, Cerebrovascular Accident, Stroke Risk factors, Classification, Kernel Functions.


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DOI: https://doi.org/10.26483/ijarcs.v1i3.60

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