Development of an AI-Based Self-Diagnosis System for Tropical Diseases

Muhammad Haris Nasri, Bayu Wibisanaa, Lilik Widyawati

Abstract


Tropical diseases are common in tropical and subtropical regions and are often difficult to diagnose due to symptoms that resemble other conditions and limited access to healthcare facilities. This study aims to develop a self-diagnosis application that integrates the Certainty Factor and Dempster-Shafer methods to improve the accuracy and reliability of tropical disease diagnosis. The application is expected to assist the public in conducting preliminary diagnoses independently, enabling timely treatment and reducing mortality and morbidity rates. The novelty of this research lies in the integration of these two artificial intelligence methods, offering an innovative solution to enhance diagnostic capabilities. The system achieved an accuracy rate of 83% based on testing, with the Certainty Factor method addressing uncertainty in symptom inputs, while the Dempster-Shafer method complements its limitations by managing uncertainty more adaptively. This combination enables more comprehensive diagnostic recommendations. However, challenges remain in cases with limited symptom data, indicating the need for further development to enhance algorithm performance and expand the clinical database

Keywords


Penyakit Tropis; Sistem Pakar; Certainty Factor; Dempster-Shafer

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References


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DOI: https://doi.org/10.32520/stmsi.v14i3.4650

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