Background: Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity.
Objectives: this study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and identify important variables in constructing the model through permutation importance.
Methods: By using the recursive feature elimination, cross-validated technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining RandomForest, XGBoost, and LightGBM algorithms was constructed. The predictive performance and permutation importance of each variable were evaluated.
Results: The variables selected to construct the model were age, sex, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and sex (0.034).
Conclusion: The performance of the model for predicting OA was relatively good. If this model is continuously used and updated, it could be used to predict OA diagnosis, and the predictive performance of the OA model may be further improved.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/jep.14195 | DOI Listing |
Epidemics
December 2024
Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, United States.
Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC's National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal.
View Article and Find Full Text PDFDiagn Interv Radiol
January 2025
Huadong Hospital, Fudan University, Department of Thoracic Surgery, Shanghai, China.
Purpose: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.
Methods: A total of 63 eligible participants were included and randomized into training and validation groups.
J Chem Inf Model
January 2025
Max-Planck-Institut für Immunbiologie und Epigenetik (MPI-IE), Stübeweg 51, 79108 Freiburg im Breisgau, Germany.
Intrinsically disordered regions are found in most eukaryotic proteins and are enriched with positively and negatively charged residues. While it is often convenient to assume that these residues follow their model-compound p values, recent work has shown that local charge effects (charge regulation) can upshift or downshift side chain p values with major consequences for molecular function. Despite this, charge regulation is rarely considered when investigating disordered regions.
View Article and Find Full Text PDFEnviron Sci Technol
January 2025
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta T6G 1H9, Canada.
The ubiquitous distribution of microplastics (MPs) in aquatic environments is linked to their transport in rivers and streams. However, the specific mechanism of bedload microplastic (MP) transport, notably their stochastic behaviors, remains an underexplored area. To investigate this, particle tracking velocimetry was employed to examine the continuous near-bed movements of four types of MPs under nine setups with different experimental conditions in a laboratory flume, with an emphasis on their streamwise transport.
View Article and Find Full Text PDFACS EST Air
January 2025
Department of Earth Sciences, University of Southern California, Los Angeles, California 90089, United States.
Computational models of atmospheric composition are not always physically consistent. For example, not all models respect fundamental conservation laws such as conservation of atoms in an interconnected chemical system. In well performing models, these unphysical deviations are often ignored because they are frequently minor, and thus only need a small nudge to perfectly conserve mass.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!