Abstract
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.
Funder
New Frontier Research Fund
Subject
Infectious Diseases,Public Health, Environmental and Occupational Health,General Immunology and Microbiology
Reference96 articles.
1. Zadeh, L.A. (1996). Fuzzy Logic, Neural Networks, and Soft Computing. Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, World Scientific.
2. An Overview of Soft Computing;Ibrahim;Procedia Comput. Sci.,2016
3. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review;Yassin;Comput. Methods Programs Biomed.,2018
4. Image-Based Cardiac Diagnosis with Machine Learning: A Review;Martin-Isla;Front. Cardiovasc. Med.,2020
5. The Role of Machine Learning Algorithms for Diagnosing Diseases;Ibrahim;J. Appl. Sci. Technol. Trends,2021
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