← Back to all news
arXiv AI By Hasan Samadbin, Arman Daliri

Right Choice of Classification Algorithms Based on Reinforcement Learning for Prediction of Non-Alcoholic Fatty Liver

Read the original on arXiv AI →

The paper proposes a reinforcement‑learning framework, termed Fourth Degree Learning, that automatically selects the most suitable classification algorithm for disease prediction tasks. Using this method, the authors report a significant improvement in predictive accuracy for Primary Biliary Cirrhosis, raising it from 63% to 98%. The approach relies on a scoring mechanism called square learning (SL) to guide the algorithm selection process.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

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
More like this →
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
More like this →