EXPERT SYSTEM WITH DEMPSTER-SHAFER METHOD FOR EARLY IDENTIFICATION OF DISEASES DUE TO COMPLICATIONS SYSTEMIC INFLAMMATORY RESPONSE SYNDROME

Adanti Wido Paramadini; Dasril Aldo; M. Yoka Fathoni; Yohani Setiya Rafika Nur; Dading Qolbu Adi

doi:10.52436/1.jutif.2024.5.3.2021

Adanti Wido Paramadini Biomedical Engineering, Faculty of Telecommunications and Electronics Engineering, Institut Teknologi Telkom Purwokerto, Indonesia
Dasril Aldo Informatics Engineering, Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia
M. Yoka Fathoni Information Technology, University Kuala Lumpur, Malaysia
Yohani Setiya Rafika Nur Informatics Engineering, Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia
Dading Qolbu Adi Informatics Engineering, Faculty of Informatics, Institut Teknologi Telkom Purwokerto, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.3.2021

Keywords: Clinical Data Analysis, Dempster-Shafer, Expert System, Medical Decision Support System, Systemic Inflammatory Response Syndrome (SIRS)

Abstract

Systemic Inflammatory Response Syndrome (SIRS) is a generalized inflammatory condition, triggered by various factors such as infection or trauma, which can lead to serious complications if not treated quickly. This condition is characterized by symptoms such as fever or hypothermia, tachycardia, tachypnea, and changes in white blood cell count. Complications that can arise from SIRS include Acute Respiratory Distress Syndrome (ARDS), which results in fluid in the alveoli and requires mechanical ventilation; acute encephalopathy, which leads to brain dysfunction; Asidosis Metabolik, indicating liver damage; hemolysis, which results in the breakdown of red blood cells; and Deep Vein Thrombosis (DVT), which is at risk of causing pulmonary embolism. To overcome this diagnostic challenge, this study implements the Dempster-Shafer method in an expert system, where it allows the aggregation and combination of various sources of evidence to produce degrees of belief and degrees of plausibility for each diagnostic hypothesis. By accounting for uncertainties and contradictions in the data, the system improves diagnostic accuracy through dynamically weighting and updating beliefs based on available evidence. This process allows early and accurate identification of SIRS complications, supporting appropriate medical intervention. System evaluation showed diagnostic accuracy of 93%, confirming the potential of expert systems in supporting rapid and precise clinical decision-making in managing SIRS complications.

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