ArticleiScience2025
Chronic liver disease classification using deep learning with SHAP-optimized hybrid features.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- DeepFusion-ACSM: an interpretable hybrid ML-DL ensemble model for anticancer small-molecule activity prediction.Journal of computer-aided molecular design · 2026Article
- ESM2-Kcr: a novel model combining protein language model and deep learning methods for predicting lysine crotonylation sites.Journal of computer-aided molecular design · 2026Article
- Peptide language pragmatic analysis and two-stage hierarchical learning framework for therapeutic peptide prediction.BMC biology · 2026Article
- Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning based individualized cross-platform molecular subtype classification of B-lineage acute lymphoblastic leukemia.NPJ precision oncology · 2026Article
- Development and validation of an early risk prediction model based on inflammatory and metabolically derived markers for early diagnosis of diabetic kidney disease.Scientific reports · 2026Article
- LightDTA: lightweight drug-target affinity prediction via random-walk network embedding and knowledge distillation.Molecular diversity · 2026Article
- Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction.Scientific reports · 2026Article
- Metabolic dysfunction-associated steatotic liver disease as a systemic disorder: extrahepatic manifestations and the need for data-driven phenotyping.Frontiers in endocrinology · 2026Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The liver is a vital organ responsible for essential functions, including digestion, metabolism, detoxification, and immunity. Liver disorders, whether due to disease, injury, or congenital conditions, pose serious health risks and require timely diagnosis for effective treatment and improved survival. Advances in machine learning (ML), particularly deep learning, have demonstrated significant potential for disease prediction, offering clinicians more accurate and efficient diagnostic tools. In this study, we propose a novel predictive framework based on a deep neural network (DNN) integrated with feature ranking and projection-based algorithms for accurate liver disease detection. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were applied to identify the most influential features affecting predictions. Experimental results indicate that the proposed DNN model outperforms traditional ML algorithms and state-of-the-art methods, achieving an average accuracy (ACC) of 92.50% under 10-fold cross-validation. These results emphasis its potential to improve diagnostic ACC, support early intervention, and enhance patient outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.