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arXiv Machine Learning

Evaluating LLM-Generated Rules for Heart Disease Prediction

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arXiv Machine Learning
Aug 20

Transforming Heart Disease Prediction with Advanced Machine Learning Techniques

The study evaluates several machine‑learning classifiers for predicting heart disease using two public datasets, each with 14 health‑related attributes. Performance metrics such as MAE, RAE, accuracy, precision, recall, and F‑measure were used, revealing that SVM performed best on the UCI data while Simple Cart excelled on the Kaggle data. The authors conclude that well‑tuned ML models can aid early heart‑disease diagnosis and suggest future work on hybrid methods and newer datasets.

By Sami Ullah, Muhammad Mohsin Khan
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arXiv Machine Learning
Jun 2

Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores

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
benchmarks
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arXiv Machine Learning
5d ago

Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization

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
benchmarks
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arXiv Machine Learning
1d ago

From Latent Biomarkers to Clinical Rules: Embedding-Guided Rule Mining and Attribution-Based Translation for Interpretable Tabular Learning

The paper introduces a four-step pipeline that mines decision rules in the latent space of an FT-Transformer and then translates those rules back into measurable clinical features. By treating embedding dimensions that separate patient groups as latent biomarkers, small decision trees are used to extract rules, which are then mapped to raw features using gradient-input saliency and CLS attention attribution. Across six public clinical datasets, the translated rules generally outperformed raw-feature rules, achieving significant AUROC gains, though some high-performing latent rules could not be fully captured by simple raw-feature conditions.

By Majid Lotfian Delouee, Hamed Ayoobi, Sjors G. J. G. In 't Veld, Martijn C. Schut
llmsragdiffusion
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