The predictive value of machine learning models based on RANKL and OPG for the risk of bone loss in patients with ulcerative colitis

Feature Importance and Growth Rate Prediction in SiC PVT Processes through Advanced Machine Learning Models

Amir Reza Ansari Dezfoli

Silicon carbide is a key wide-bandgap semiconductor material for next-generation power electronics, yet the Physical Vapor Transport (PVT) method used for bulk crystal growth remains constrained by complex thermal-chemical interactions and low growth rates. This study develops a…Read more

Also available via: European Organization for Nuclear Research

Predictive Value of Different Machine Learning Models for Clinical Pregnancy Outcomes in Patients with Polycystic Ovary Syndrome Undergoing IVF/ICSI Treatment

Guzainuer Ailiniyazi

Also available via: European Organization for Nuclear Research

Comparative Analysis of Machine Learning Classification Algorithms and Hybrid Models for Student Performance Prediction

Ms. Pooja C. Soni, Dr. Hetal R. Modi, PC Negi

This study focuses on the analysis and comparison of machine learning classification algorithms and hybrid machine learning models for predicting student academic performance. Educational Data Mining techniques are used to extract meaningful insights from student datasets. Variou…Read more

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment"

Lennart John Baals, Yiting Liu, Joerg Osterrieder, Branka Hadji Misheva

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Syste…Read more

Also available via: European Organization for Nuclear Research

Federated Machine Learning for Privacy-Preserving Healthcare Risk Prediction

Jnana Durga Ravi Chand Mutthina, Sriharsha Doniparthi, Ramya Kata

This preprint presents an Explainable Federated Machine Learning framework, called XFML, for privacy-preserving diabetes risk prediction across distributed healthcare institutions. The framework enables multiple hospital nodes to collaboratively develop a predictive model without…Read more

Reproducibility Package for Explainable and Leakage-Conscious Machine Learning for Athlete Injury Risk Modeling Across Heterogeneous Datasets

Abdülkadir Enes GÖRGÜLÜ, Eray Dursun, Serdar SOLAK

This reproducibility package supports the manuscript “Explainable and Leakage-Conscious Machine Learning for Athlete Injury Risk Modeling Across Heterogeneous Datasets.” It contains the executed and clean analysis notebooks, the corresponding Python script, exact software-version…Read more