arXiv:2601. 00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using routinely collected electronic health record (EHR) data and benchmark their performance against the FIB-4 and APRI clinical scores.
By Zhuqi Miao, Ahmed G Qasem, Sujan Ravi, Jason T. Cheng, Abdulaziz Ahmed, Courtney W. Houchen, Sumayah Abed, Dilorom Azimdjanovna Zuparova, Abdulaziz Ahmed
arXiv:2606. 16056v1 Announce Type: new Abstract: Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed.
By Black Sun, Chenyi Zhang, Kaiyi Ji, Xi Lu
arXiv:2601.00175v3 Announce Type: replace
Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years before diagnosis using...
By Zhuqi Miao, Ahmed G Qasem, Sujan Ravi, Jason T. Cheng, Abdulaziz Ahmed, Courtney W. Houchen, Sumayah Abed, Dilorom Azimdjanovna Zuparova, Abdulaziz Ahmed
arXiv:2606. 26561v1 Announce Type: new Abstract: Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver.
By Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy, Salam Alshammasi, Meshael Almusairii, Shoog Alrassan, Aisha Alansari
Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis.
arXiv:2607. 16253v1 Announce Type: cross Abstract: Machine learning-based Type 2 diabetes risk prediction models obtain good internal validation results but lose effectiveness in real-world applications due to deficient external testing and fairness assessment.
By Rajveer Singh Pall, Sameer Yadav, Siddharth Bhalerao, Sourabh Sahu, Ritu Ahluwalia, Bhaskar Awadhiya
This study investigates deep learning and explainable AI methods to diagnose hepatocellular carcinoma (HCC) and identify diagnostic and prognostic biomarkers across five disease stages using a transcriptomic dataset built via semi‑supervised learning. The best model used 15 genes selected by SelectKBest, achieving 90.74% accuracy, while a 20‑gene model had the lowest loss of 0.3187. SHAP‑based XAI highlighted DNAJB14 as the most influential gene, and functional validation showed that inhibiting DNAJB14 reverses key malignant traits of HCC cells.
By Ali Bou Nassif, Darko Castven, Manar Abu Talib, Jibran Sualeh Muhammad, Ahmed Ammar Kubba, Jens Marquardt, Abdalla Sayed Ali
arXiv:2606. 30702v1 Announce Type: cross Abstract: Structured tabular data dominates clinical medicine, yet existing benchmarks fail to reflect real-world properties like complex survey sampling, demographic oversampling, and subgroup fairness.
By Federico Felizzi
The paper introduces a statistically grounded framework for interpretable, rule-based clinical classification using Bernoulli Naïve Bayes (BNB). It employs supervised chi‑square‑guided binarization to convert continuous medical variables into binary indicators, enabling BNB to handle continuous data while maintaining transparency. On three benchmark datasets—Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction—the method achieved AUCs of 0.800, 0.984, and 0.919, respectively, and demonstrated reliable probability calibration through cross‑validated analysis and post‑hoc beta calibration.
By Antony Garcia, Adrian Noriega, Gabrielle Britton, Xinming Huang
The study evaluates whether inflammatory biomarkers can predict cognitive impairment in older Hispanic adults using interpretable machine learning on a small clinical dataset. A leakage‑safe Bernoulli/Categorical Naive Bayes model was trained on 165 participants from the Panama Aging Research Initiative, with continuous predictors discretized via supervised chi‑square and income treated categorically. The biomarker I‑309 (CCL1) emerged as the sole reliable incremental predictor, boosting ROC‑AUC from 0.630 to 0.740 and achieving statistically significant performance across repeated cross‑validation and random partitions.
By Antony Garcia, Gabrielle Britton, Alcibiades Villarreal, Diana Oviedo, Giselle Rangel, Xinming Huang
arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.
By Antony Garcia, Adrian Noriega, Gabrielle Britton, Xinming Huang
arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.
By Wei Xu, Ke Yang, Gang Luo, Keli Zheng, Lingyan Hu, Jing Wang, Kefeng Li