Abstract
Diabetes is one of the most common disease in today’s life. It is affecting people with a high rate, destroying person’s physical, mental, economic and family life. Diabetes is a disease when the normal metabolic process is affected by an increase in the blood sugar level. The disease falls under the chronic category and is said to be the 7th leading cause of death, according to American Diabetes Association (ADA).In this manuscript, Pima India Diabetes Dataset is taken from the UCI Repository for the analysis purpose. The study used Naïve Bayes and Support Vector Machine as classification models along with feature selection for improving the accuracies of the model. Result evaluation is done based on accuracy, precision and recall values. Enhanced performance of the model is calculated using k-fold cross-validation technique. Experimental result shows that the performance of Support Vector Machine is better than Naïve Bayes model.
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Gupta, S., Verma, H.K., Bhardwaj, D. (2021). Classification of Diabetes Using Naïve Bayes and Support Vector Machine as a Technique. In: Sachdeva, A., Kumar, P., Yadav, O., Garg, R., Gupta, A. (eds) Operations Management and Systems Engineering . Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-6017-0_24
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DOI: https://doi.org/10.1007/978-981-15-6017-0_24
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