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Comparison of Naïve Bayes and Random Forest Methods for Diabetes Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 174 - Number 26 |
| Year of Publication: 2021 |
| Authors: Winda Hasanah, Lulu Chaerani Munggaran |
10.5120/ijca2021921184
|
Winda Hasanah, Lulu Chaerani Munggaran . Comparison of Naïve Bayes and Random Forest Methods for Diabetes Prediction. International Journal of Computer Applications. 174, 26 ( Mar 2021), 13-18. DOI=10.5120/ijca2021921184
Abstract
Diabetes is a chronic metabolic disorder in which blood sugar levels exceed normal limits. Riskesdas Ministry of Health in 2018 showed the prevalence of diabetes mellitus in Indonesia increased from 2013. Classification is one of the solutions to decrease the prevalence of diabetes in Indonesia. In this research, Classification is used to predict diabetes by building a classification model. The research steps are data collection, split the dataset into training data and test data, build a classification model using the Naïve Bayes and Random Forest methods, and evaluate the model. The results showed that the Random Forest method has the best performance with accuracy = 100%, error = 0%, precision = 1 and recall = 1. The best ratios in classifying the diabetes dataset are 70:30 and 90:10.
References
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