IEEE

Research on Multi-Dimensional Prediction Methods Based on Random Forest and Xgboost Algorithms

Abstract: This study established a multi-dimensional prediction framework centred on random forest and XGBoost. Firstly, baseline prediction values are generated using a theoretical model, combined ...
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

Parkinson’s Disease Identification By Voice and Handwritten Drawings using Xgboost and Random Forest Algorithms

Abstract: Parkinson's disease (PD) is a neurological movement illness that starts with a subtle tremor over hand and a feel of stiffness across the body. It affects over than 6 million people ...
GitHub

rskworld/fraud-detection-ml

Advanced fraud detection system using machine learning to identify fraudulent transactions and activities. This project implements multiple machine learning algorithms including Random Forest, XGBoost ...
GitHub

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression ...

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Scientific Research Publishing

Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32.

ABSTRACT: Atrial fibrillation (AF) is a leading cardiac arrhythmia associated with elevated mortality risk, particularly in low-resource settings where early risk stratification remains challenging.