Comparison of Dengue Predictive Models Developed Using Artificial Neural Network and Discriminant Analysis with Small Dataset

Applied Sciences

卷:11

Wibowo Mangunwardoyo¹  Permatasari Silitonga²  Alhadi Bustamam²  Hengki Muradi³  BetiE. Dewi⁴ 

[1] Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia;

[2] Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus Baru UI, Depok 16424, Indonesia;

[3] Department of Mathematics, Faculty of Science and Information Technology, Institut Sains dan Teknologi Nasional, Jl.Moh Kahfi II Srengseng Sawah Jagakarsa, Jakarta Selatan 12640, Indonesia;

[4] Department of Microbiology, Faculty of Medicine, Universitas Indonesia, Jl. Salemba Raya no. 5, Kota Jakarta Pusat, Daerah Khusus Ibu Kota Jakarta 10430, Indonesia;

关键词: Artificial Neural Network;    Discriminant Analysis;    dengue;   

DOI  :  10.3390/app11030943

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown