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

Interpretable machine-learning risk stratification at diagnosis for 3-year mortality in de novo metastatic prostate cancer (SEER): reproducibility code

Xin Wang, Guanglei Yao, Wei Ding

This archive contains the analysis code, the predictor dictionary, and the retrained primary model objects underlying the manuscript "Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality in de novo metastatic prostate cancer: developmen…Read more

A Systematic Review of Machine Learning, Deep Learning, and Explainable AI Approaches for Cardiac Disease Prediction

Sunanda Budihal, Sheetalrani Kawale, Abhishek Angadi

The cardiovascular (Cardiac) disease (CVD) is another factor that causes death among the global population most, and this is the reason why there is a high necessity to implement proper, effective, and interpretive diagnostic systems. The usage of machine learning (ML), deep lear…Read more

Also available via: European Organization for Nuclear Research

Before the Model: Why Datasets and Data Representation Define What Machine Learning Can Learn

Jean Franck Loa Rojas

Machine learning systems do not learn reality directly; they learn from the representations preserved in their datasets. This structured narrative review examines how dataset purpose, coverage, integrity, labeling, independence, reproducibility, governance, and continuity determi…Read more

An Explainable Machine Learning Model and Bedside Nomogram Support Hemodialysis Decision-Making in Lithium Poisoning

Kamran Rezaei, Shahin Shadnia, Babak Mostafazadeh, Mitra Rahimi, Peyman erfantalabevini, Seyed Masoud Hosseini, et al.

The python codes for evaluation of a dataset containing lithium poisoning patients' data. Four Machine Learning models were used (Elastic-Net logistic regression (LR), linear support vector machine (SVM), shallow artificial neural network (ANN), and constrained Random Forest). Ea…Read more

Also available via: European Organization for Nuclear Research

Reproducibility package for Explainable and Leakage-Conscious Machine Learning for Supplied Injury-Risk Classification and Longitudinal Athlete Injury Forecasting

Abdülkadir Enes GÖRGÜLÜ, Eray Dursun, Serdar Solak

This record provides the complete reproducibility package for the manuscript “Explainable and Leakage-Conscious Machine Learning for Supplied Injury-Risk Classification and Longitudinal Athlete Injury Forecasting.” Overview The study evaluates explainable and leakage-conscious ma…Read more

Financial Distress Prediction in Mature Markets: A Machine Learning Approach across G7 Economies

Varunn Kaushik

Abstract: This study examines the determinants and predictive accuracy of financial distress for seven mature market economies: Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. The aim is to assess whether distress can be predicted through a unifo…Read more