ArticleFrontiers in medicine2025
Hybrid intelligent systems for liver disease prediction: a demographic-aware machine learning framework.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Single-cell spatial landscape of aggrephagy activity stratifies hepatocellular carcinoma neutrophils and delivers a 5-gene diagnostic panel for patient stratification.Translational oncology · 2026Article
- Molecular landscape and clinical translation of DNA damage response alterations in solid tumors: A pan-cancer perspective on precision oncology.Neoplasia (New York, N.Y.) · 2026Review
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Authors and funding
9 authors.
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Abstract
Background: Liver disease remains a major global health burden, often progressing undetected until advanced stages. Traditional diagnostic approaches, while accurate, are invasive, costly, and limited in accessibility. Objective: To address these challenges, we propose a hybrid intelligent framework that integrates demographic segmentation with advanced machine learning for the early detection of liver disease. Results: Two datasets were employed, including the Indian Liver Patient Dataset (ILPD, Conclusion: By combining demographic awareness, hybrid learning, and interpretability, this study offers a scalable, non-invasive, and clinically relevant tool for early detection of liver diseases, advancing personalized and accessible healthcare.
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