期刊
TROPICAL MEDICINE AND INFECTIOUS DISEASE
卷 7, 期 12, 页码 -出版社
MDPI
DOI: 10.3390/tropicalmed7120398
关键词
medical decision support systems; soft computing; tropical diseases; medical records; telemedicine; health science
资金
- New Frontier Research Fund
- [NFRFE-2019-01365]
This systematic literature identifies the use of soft computing techniques in diagnosing tropical febrile diseases and analyzes their impact on physician care quality and effectiveness. The study finds that ensemble techniques and neural networks are commonly used, with dengue fever being the most studied disease. Accuracy is the primary metric for evaluating classification models. The findings benefit frontline healthcare workers and suggest the need for further research.
This systematic literature aims to identify soft computing techniques currently utilized in diagnosing tropical febrile diseases and explore the data characteristics and features used for diagnoses, algorithm accuracy, and the limitations of current studies. The goal of this study is therefore centralized around determining the extent to which soft computing techniques have positively impacted the quality of physician care and their effectiveness in tropical disease diagnosis. The study has used PRISMA guidelines to identify paper selection and inclusion/exclusion criteria. It was determined that the highest frequency of articles utilized ensemble techniques for classification, prediction, analysis, diagnosis, etc., over single machine learning techniques, followed by neural networks. The results identified dengue fever as the most studied disease, followed by malaria and tuberculosis. It was also revealed that accuracy was the most common metric utilized to evaluate the predictive capability of a classification mode. The information presented within these studies benefits frontline healthcare workers who could depend on soft computing techniques for accurate diagnoses of tropical diseases. Although our research shows an increasing interest in using machine learning techniques for diagnosing tropical diseases, there still needs to be more studies. Hence, recommendations and directions for future research are proposed.
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