This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are cre...
Rapid and accurate detection of glutathione in its reduced (GSH) and oxidized (GSSG) forms is essential for monitoring oxidative stress in biological systems. Oxidative stress is a key indicator of various diseases, and glutathione plays a vital role...
Urinary incontinence affects many women, yet there are no monitoring devices capable of accurately capturing flow dynamics during everyday activities. Building on our initial development of a wearable personal uroflowmeter, this study enhances the de...
Current orthopedic robots lack the ability to dynamically sense or accurately recognize bone layers during vertebral plate decompression surgery, limiting their ability to adjust actions in real time as skilled surgeons do. This study aims to improve...
To tackle work-related stress in the evolving landscape of Industry 5.0, organizations need to prioritize employee well-being through a comprehensive strategy. While electrocardiograms (ECGs) and electrodermal activity (EDA) are widely adopted physio...
Analytical methods : advancing methods and applications
May 9, 2025
In this study, hyperspectral technology along with a combination of squeeze-and-excitation convolutional neural networks and competitive adaptive reweighted sampling (CARS-SECNNet) was developed to identify sorghum varieties. In addition, the support...
This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healt...
An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This study aimed at developing and validating externally patient-wise prognostic models of the global fu...
High-grade gliomas, particularly glioblastoma (MeSH:Glioblastoma), are among the most aggressive and lethal central nervous system tumors, necessitating advanced diagnostic and prognostic strategies. This systematic review and epistemic meta-analysis...
PURPOSE: To evaluate machine learning-based survival model roles in predicting rehospitalization after hip fractures to improve reduce the burden on the healthcare system.
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