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MathWorks Inc machine learning model development
Machine Learning Model Development, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 2323 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/statistical+and+machine+learning+models/Statistics+and+Machine+Learning+Toolbox/pm41548988-143-5-12
Average 96 stars, based on 2323 article reviews

machine learning model development - by Bioz Stars, 2026-10

96/100 stars

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Article Title: Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project.
Article Snippet: Statistical analysis and ML techniques were implemented in MATLAB R2023a (feature section: fscchi2 function from the Statistics and Machine Learning Toolbox, version 2023a; MLR models: fitglm function from the Statistics and Machine Learning Toolbox, version 2023a), whereas the data imputation technique was implemented in RStudio (Build 492) using the imputeFAMD function from the missMDA package (version 1.11).

Article Title: Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.
Article Snippet: Statistical analysis and machine learning classification models were established using MATLAB (R2022b).

Article Title: Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project
Article Snippet: Statistical analysis and ML techniques were implemented in MATLAB R2023a (feature section: fscchi2 function from the Statistics and Machine Learning Toolbox, version 2023a; MLR models: fitglm function from the Statistics and Machine Learning Toolbox, version 2023a), whereas the data imputation technique was implemented in RStudio (Build 492) using the imputeFAMD function from the missMDA package (version 1.11).

Article Title: Personalized Assessment of Mortality Risk and Hospital Stay Duration in Hospitalized Patients with COVID-19 Treated with Remdesivir: A Machine Learning Approach
Article Snippet: We built the models using the MatLab Statistical and Machine Learning Toolbox (MatLab 2022a; The MathWorks, Natick, MA, USA).

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Journal: bioRxiv

Article Title: Stroke-Related Changes in Tonic and Phasic Muscle Recruitment During Reaching Reveal Pathway-Specific Motor Deficits

doi: 10.1101/2025.05.28.656732

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Article Snippet: To test these hypotheses, we fitted a non-parametric generalized linear mixed-effects (GLME) model ( fitglme function in Statistics and Machine Learning Toolbox, MATLAB) with an identity link and normal error distribution to examine the effects of three continuous predictors—Component (Tonic vs. Phasic), Direction (Outward vs. Inward), and Muscle Group (Proximal, Biarticular, vs. Distal)—on the sum of score tunings, along with their interactions.

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