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All Journal Journal of Intelligent Computing and Health Informatics (JICHI)
Muhammad Sam'an
Universitas Muhammadiyah Semarang
Computer Science & IT Dentistry Electrical & Electronics Engineering Medicine & Pharmacology Public Health

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A Robust Hybrid Cost Sensitive Stacking Ensemble Model for Hepatitis Survival Prediction and Clinical Decision Support Muhammad Sam'an; Farikhin Farikhin
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.17519

Abstract

Chronic hepatitis continues to pose a significant global health challenge, frequently advancing to liver cirrhosis and hepatocellular carcinoma if not managed with precise prognostic interventions. The capacity to accurately predict patient survival is essential for optimizing resource allocation and treatment planning. Although Machine Learning (ML) has shown promise in medical diagnostics, standard algorithms often underperform when applied to hepatitis datasets characterized by severe class imbalance and high dimensionality. Conventional models tend to bias predictions toward the majority class (survival), resulting in a high rate of False Negatives for the minority class (mortality), which is clinically unacceptable. Moreover, single-classifier approaches often lack the generalization capability necessary for robust clinical deployment. To address these deficiencies, this study proposes a Hybrid Cost-Sensitive Stacking Ensemble Model (HCS-SEM). The framework integrates three strategic components: (1) a rigorous Split-First Synthetic Minority Oversampling Technique (SMOTE) protocol to resolve class skewness without data leakage; (2) a Chi-Square feature ranking mechanism to eliminate redundant clinical attributes; and (3) a Two-Tier Stacking Architecture employing Random Forest, SVM, and Gradient Boosting as base learners, optimized by a Logistic Regression meta-learner. Experimental validation on the UCI Hepatitis dataset demonstrates that HCS-SEM significantly outperforms standalone classifiers and traditional ensemble methods. The model achieves superior performance metrics, particularly in Sensitivity and F1-Score, confirmed by the Friedman Rank Test and Nemenyi post-hoc analysis. These findings suggest that the proposed HCS-SEM provides a robust, clinically viable tool for hepatitis prognosis, offering high-precision decision support for medical practitioners managing high-risk patients.
The Hybrid Residual–BiLSTM Model for Mitigating the Vanishing Gradient Problem in Synthetic Image Detection Kafitra Marna Ibrahim; Brian Halubanza; AHMAD SHUKRI MOHD NOOR; Muhammad Sam'an; Muhammad Munsyarif; Azyan Yusra Kapi
Journal of Intelligent Computing & Health Informatics Vol 7, No 2 (2026): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v7i2.22744

Abstract

The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic synthetic images, raising concerns regarding the authenticity of digital media. While convolutional neural network (CNN)-based methods have shown promising results for synthetic image detection, deep architectures often suffer from training instability caused by the vanishing gradient problem and limited representation learning. This study proposes a Deep Residual BiLSTM Fusion model that integrates residual-based CNN feature extraction with bidirectional long short-term memory (BiLSTM) modeling to enhance training stability and contextual representation learning. The proposed model is evaluated on the CIFAKE dataset, a challenging low-resolution benchmark for synthetic image detection. Experimental results demonstrate competitive performance with an accuracy of 97.27% and balanced precision and recall across both real and synthetic classes. Gradient flow analysis further confirms stable optimization behavior, highlighting the effectiveness of the proposed hybrid architecture for robust synthetic image detection.
Co-Authors AHMAD SHUKRI MOHD NOOR Azyan Yusra Kapi Brian Halubanza Farikhin Farikhin Kafitra Marna Ibrahim Muhammad Munsyarif