Exercise & Fitness

Identification of biomarkers for knee osteoarthritis through clinical data and machine learning models.

Knee osteoarthritis (KOA) represents a progressive degenerative disorder characterized by the gradual erosion of articular cartilage. This study aimed to develop and validate biomarker-based predictive models for KOA diagnosis using machine learning techniques. Clinical data from 2594 samples were obtained and stratified into training and validation datasets in a 7:3 ratio. Key clinical features w...

Twelve tips on applying AI tools in HPE scholarship using Boyer's model.

AI has changed the landscape of health professions education. With the hype now behind us, we find ourselves in the phase of reckoning, considering what's next; where do we start and how can educators use these powerful tools for daily teaching and learning. We recognize the great need for training to use AI meaningfully for education. Boyer's model of scholarship provides a pedagogical approach f...

Prediction of p-phenylenediamine antioxidant concentrations in human urine using machine learning models.

p-phenylenediamine antioxidants (PPDs) are extensively used in rubber manufacturing for their potent antioxidative properties, but PPDs and 2-anilino-...

Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination.

With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue...

The impact of preschool children's physical fitness evaluation under self organizing maps neural network.

To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing...

Polymer Biodegradation in Aquatic Environments: A Machine Learning Model Informed by Meta-Analysis of Structure-Biodegradation Relationships.

Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural envir...

A Fusion Network With Stacked Denoise Autoencoder and Meta Learning for Lateral Walking Gait Phase Recognition and Multi-Step-Ahead Prediction.

Lateral walking gait phase recognition and prediction are the premise of hip exoskeleton application in lateral resistance walk exercise. We presented...

Fuzzy Synchronization Likelihood Graph in Deep Neural Networks for Human Motion Time Series Analysis.

Variable interactivity is crucial in biological multivariate time series analysis. This research suggests using graph structures to represent such int...

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.

Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely i...

Finding Predictive Factors of Stabilization Exercise Adherence in Randomized Controlled Trials on Low Back Pain: An Individual Data Reanalysis Using Machine Learning Techniques.

OBJECTIVE: To identify predictors of adherence in supervised and self-administered exercise interventions for individuals with low back pain.

AI and Uncertain Motivation: Hidden allies that impact EFL argumentative essays using the Toulmin Model.

This study investigates the combined impact of artificial intelligence (AI) tools and Uncertain Motivation (UM) strategies on the argumentative writin...

Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task.

Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introd...

Exercise improves body composition, physical fitness, and blood levels of C-peptide and IGF-1 in 11- to 12-year-old boys with obesity.

INTRODUCTION: Exercise is vital in preventing and treating obesity. Despite its importance, the understanding of how exercise influences childhood obe...

Utilising causal inference methods to estimate effects and strategise interventions in observational health data.

Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RC...

Digital phenotyping from heart rate dynamics: Identification of zero-poles models with data-driven evolutionary learning.

Heart rate response to physical activity is widely investigated in clinical and training practice, as it provides information on a person's physical s...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique...

A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical present...

Breath Analyzer for Real-Time Exercise Fat Burning Prediction: Oral and Alveolar Breath Insights with CNN.

The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fa...

Optimizing hypertension prediction using ensemble learning approaches.

Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study investigates how well ensemble learning technique...

Machine learning analysis of factors affecting college students' academic performance.

This study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning m...