The Comparative Study for Predicting Disease Outbreak

Authors

  • anifatul faricha ITTelkom Surabaya
  • M. Achirul Nanda
  • Siti Maghfirotul Ulyah
  • Ni'matut Tamimah
  • Enny Indasyah
  • Robin Addwiyansyah Alvaro Samrat

Keywords:

Dataset; Optimizable GPR; Optimizable SVM.

Abstract

To know the prediction of disease outbreak, proper predictive modeling is required to represent the dataset. This study presents the comparative predictive modeling for predicting disease outbreak using two models i.e., optimizable support vector machine (SVM) and optimizable gaussian process regression (GPR). The dataset used in this study contains three cases i.e., positive cases, recovered cases, and death cases. The dataset at each case is divided into training dataset for the training process and external validation dataset for the validation process. Based on the training process and validation process, the root mean square error (RMSE) at positive cases, recovered cases, and death cases using optimizable GPR is substantially more effective for prediction than the optimizable SVM. According to the result performance, by applying optimizable GPR, the training process has the average RMSE of 19.54 and the validation process has the average RMSE of 15.85.

Published

2020-07-15

Issue

Section

Articles