Frontiers

Explainable machine learning for stroke risk prediction: a comparative study with SHAP-based interpretation

Background: Stroke is one of the leading causes of death and disability worldwide, making early screening and risk prediction crucial. Traditional methods have limitations in handling nonlinear ...
Open Heart

Machine learning-based cardiovascular risk calculator for non-cardiac surgery

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...
IEEE

Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics

Abstract: In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a ...
Scientific Research Publishing

Tassi, A., Gigante, D., Modica, G., Di Martino, L. and Vizzari, M. (2021) Pixel-vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The ...

ABSTRACT: Accurate land cover classification is essential for environmental monitoring, urban planning, and resource management. Conventional classifiers trained on raw spectral bands are often ...
Frontiers

Prediction of total iodine dose of I-131 therapy for Graves’ hyperthyroidism achieved remission status: a random forest regressor model approach to assess treatment efficacy

Department of Nuclear Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Autonomous Region, China Objective: Graves’ hyperthyroidism (GH) presents significant ...
IEEE

Enhancing Sleep Stage Classification With Single Channel EEG: Feature Extraction and Random Forest-XGBoost Model

Abstract: Sleep stage classification, essential for diagnosing sleep disorders, monitoring neurological health, and advancing cognitive neuroscience. Traditionally involves manual scoring of ...