Comparison of dengue predictive models developed using artificial neural network and discriminant analysis with small dataset

Permatasari Silitonga, Alhadi Bustamam, Hengki Muradi, Wibowo Mangunwardoyo, Beti E. Dewi

Research output: Contribution to journalArticlepeer-review

Abstract

In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided.

Original languageEnglish
Article number943
Pages (from-to)1-16
Number of pages16
JournalApplied Research on English Language
Volume11
Issue number3
DOIs
Publication statusPublished - 1 Feb 2021

Keywords

  • Artificial neural network
  • Dengue
  • Discriminant analysis

Fingerprint Dive into the research topics of 'Comparison of dengue predictive models developed using artificial neural network and discriminant analysis with small dataset'. Together they form a unique fingerprint.

Cite this