Expert System for Diagnosing Dengue Fever Using Decision Tree Classification

Aris Rakhmadi, Bangun Sri Langgeng Anggoro, Ihsan Cahyo Utomo, Muhammad Syahriandi Adhantoro, Yudi Wahyu Wibowo

Abstract


Dengue fever remains a significant public health concern, particularly in tropical regions, where early diagnosis is crucial to reducing the risk of severe complications. This study presents the development of a web-based expert system for preliminary dengue diagnosis using a decision tree classification approach. The system was built using medical record data collected from RSUD dr. Mangun Soemarso Wonogiri, comprising 150 patient cases with binary symptom attributes. A decision tree algorithm was applied to generate rule-based logic, which was then implemented in a web application that allows users to perform self-assessment through a series of yes/no symptom-related questions. Functional testing using the blackbox method confirmed that all system modules operated as intended. Rule validation ensured the correctness of the decision logic, while evaluation by a general practitioner affirmed the clinical relevance of the diagnostic rules. The expert system offers an accessible and interpretable tool for early detection of dengue fever, particularly in areas with limited access to healthcare. Further enhancements are recommended, including dataset expansion, probabilistic reasoning, and integration with telemedicine services.

Keywords


Expert System, Dengue Fever, Decision Tree, Medical Diagnosis, Rule-Based Classification

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