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Yaslinda Lin Lizar
Fakultas Sains dan Teknologi UIN Imam Bonjol Padang
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences Other

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Integrasi Deep Neural Network dan Rule-Based Reasoning dalam Sistem Pakar untuk Diagnosis Gangguan Sistem Saraf Yaslinda Lin Lizar
Insearch: Information System Research Journal Vol 6, No 02 (2026): Insearch (Information System Research) Journal
Publisher : Fakultas Sains dan Teknologi UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/isrj.v6i02.13728

Abstract

The advancement of artificial intelligence in healthcare has encouraged the use of deep learning to support medical diagnosis. However, Deep Neural Network (DNN) models suffer from low interpretability due to their black-box nature, which limits clinical applicability. This study aims to integrate DNN and Rule-Based Reasoning (RBR) into an expert system to provide accurate and explainable neurological disorder diagnosis. The dataset consists of 400 clinical patient records covering four diagnostic classes: peripheral neuropathy, transient ischemic attack (TIA), acute migraine, and epilepsy. The DNN model employs a multilayer perceptron architecture with two hidden layers and ReLU activation, while RBR applies IF–THEN rules derived from expert knowledge. The integration mechanism combines DNN probability and rule-based certainty factors through weighted scoring. Experimental results show an accuracy of 91%, precision of 0.90, recall of 0.89, and F1-score of 0.895. Expert validation indicates an 86% confidence level, demonstrating that the proposed system is suitable as an explainable artificial intelligence-based diagnostic support tool.
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