Scientific Research Publishing

Sitompul, L.R., Nababan, A.A., Manihuruk, M.L., Ponsen, W.A. and Supriyandi, S. (2025) Comparison of XGBoost, Random Forest and Logistic Regression Algorithms in Stroke Disease ...

ABSTRACT: Detecting behavioural signatures of depression from everyday digital traces is a central challenge in computational psychiatry. Real-world datasets from smartphones and wearables often ...
IEEE

AI-Driven Classification of Tsunami-Generating Earthquakes: Harnessing Random Forest, SVM, and Logistic Regression for Early Detection

Abstract: Predicting whether an earthquake will generate a tsunami is critical for early warning systems and disaster mitigation. In this study, we present an AI-driven approach to classify ...
IEEE

Machine Learning-Based Classification of Crisis-Related Tweets: A Comparative Study Using Logistic Regression, Random Forest, and BERT

Abstract: Social media has become a crucial communication medium during disasters, producing large volumes of user-generated content that, if properly filtered, can provide responders with timely and ...
Scientific Research Publishing

Tranmer, M. and Elliot, M. (2008) Binary Logistic Regression. Cathie Marsh for Census and Survey Research Paper 20.

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...