Data and code for: Frequent Mental Distress Across Texas Census Tracts: Social-Environmental Co-Exposure, Spatial Dependence, and Interpretable Machine Learning

Towards Early and Accurate Disease Detection Through Multimodal Predictive Modeling: Fusion of Electronic Health Records, Medical Imaging, And Omics Data Using Interpretable Machine Learning.

Muhammad Ahsan Hayat, Jahangir Baig, Shayan Ahmed, Ahmed Faraz Ayubi

Early detection of disease is a cornerstone for improving patient outcomes, reducing costs, and enabling preventative interventions. Traditional predictive models often rely on a single type of data (e.g., imaging, clinical labs, or genomics). However, human health is inherently…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

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

Code and data for: Leakage-audited machine learning versus ETAS for earthquake forecasting in the Sea of Marmara

Basri Kerem Alhan, Kenessary Khabat

Code, processed data products, configuration, and results artifacts for "Machine learning versus ETAS for earthquake forecasting in the Sea of Marmara: a leakage-audited negative result and a closed-form scoring artifact" (Alhan & Khabat, submitted to Seismica). Version 1.2.0 acc…Read more

An Aromatic-Vinylene Structural Correlate of CO₂ Permeability in Glassy Polymer Membranes Identified by Interpretable Machine Learning

Ghazal Saki Norouzi

This repository contains all analysis code for the manuscript submitted.

Data and code for "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative Manuscript TypeResearch Article"

Seokhwan Hwang

This repository archives the data and code accompanying the manuscript: "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative."