Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset

Autor:	Ilyas Ozer, Feyzullah Temurtas, Onursal Çetin, Kutlucan Görür
Rok vydání:	2021
Předmět:	Shallow machine learning 0209 industrial biotechnology Computer science 02 engineering and technology Machine learning computer.software_genre Convolutional neural network 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Global health Hospital patients High prevalence business.industry Deep learning Medical record Arboviral infection Linear discriminant analysis Transfer learning Original Article 020201 artificial intelligence & image processing Artificial intelligence Transfer of learning business computer Software
Zdroj:	Neural Computing & Applications
ISSN:	1433-3058 0941-0643
DOI:	10.1007/s00521-021-06133-0
Popis:	The prediction of hospital patients and outpatients with suspected arboviral infection individuals in research-limited settings of the urban areas is defined as a challenging process for clinicians. Dengue, Chikungunya, and Zika arboviruses have gained attention in recent years because of the high prevalence in the society and financial burden of major global health systems. In this study, we proposed a machine learning algorithm based prediction model over retrospective medical records, which are named as SISA (the Severity Index for Suspected Arbovirus) and SISAL (the Severity Index for Suspected Arbovirus with Laboratory) datasets. Therefore, we aim to inform the clinicians about the use of machine learning with transfer learning success for diagnosis and comprehensive comparison of the classification performances over the SISA/SISAL datasets in the resource-limited settings that may cause to the small datasets of arboviral infection. In this study, Convolutional Neural Network and Long Short-Term Memory have achieved 100% accuracy and 1 of area under the curve (AUC) score, Fully Connected Deep Network has provided 92.86% accuracy and 0.969 AUC score in the SISAL dataset with transfer learning. Moreover, 98.73% accuracy and 0.988 AUC score were obtained by Convolutional Neural Network and Long Short-Term Memory for the SISA dataset. Furthermore, Linear Discriminant Analysis (shallow algorithm) has provided reaching up to 96.43% accuracy. Notably, deep learning based models have achieved improved performances compared to the previously reported study.
Databáze:	OpenAIRE
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