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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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Clinical decision support systems (CDSSs) symbolize a significant transformation in healthcare delivery. CDSS enhances healthcare delivery by enabling personnel in medical institutions to handle complex decision-making processes with great speed and high accuracy. Decision support systems are developed using a knowledge-driven or data-driven approach, although both approaches seem to complement each other. For instance, while data-driven is an objective approach, the knowledge-driven approach is subjective. The objective of the chapter is to elaborate on the integration of data-driven and knowledge-driven methodologies for clinical decision support systems. An overview of data-driven and knowledge-driven approaches is presented with a review of both current and dated literature on the subject with numerous viewpoints to support the discussion. Based on the findings, a promising methodology is proposed that integrates data-driven and knowledge-driven approaches and is believed to overcome the challenges of the individual approaches.

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