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Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.

• Authors : Marcos-Pasero H; Nutrition and Clinical Trials Unit, GENYAL Platform IMDEA-Food Institute, CEI UAM+CSIC, 28049, Madrid, Spain.; Colmenarejo G

Subjects: Machine Learning*; Metabolic Diseases/Metabolic Diseases/Metabolic Diseases/*epidemiology ; Pediatric Obesity/Pediatric Obesity/Pediatric Obesity/*epidemiology

• Source: Scientific reports [Sci Rep] 2021 Jan 21; Vol. 11 (1), pp. 1910. Date of Electronic Publication: 2021 Jan 21.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Gdaphen, R pipeline to identify the most important qualitative and quantitative predictor variables from phenotypic data.

• Authors : Muñiz Moreno, Maria del Mar^1,2 (AUTHOR) ; Gavériaux-Ruff, Claire¹ (AUTHOR); Herault, Yann^1,3 (AUTHOR)

• Source: BMC Bioinformatics. 1/26/2023, Vol. 24 Issue 1, p1-18. 18p.

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Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables

• Authors : Rausch, Theresa Maria; Albrecht, Tobias; Baier, Daniel

Subjects: ddc:650; C22; C53

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Exploring Predictive Variables Affecting the Sales of Companies Listed With Korean Stock Indices Through Machine Learning Analysis

• Authors : Gwangsu Lee

Subjects: Economic indices; machine learning; national statistical indices

• Source: IEEE Access, Vol 11, Pp 63534-63549 (2023)

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Machine learning predictions of unplanned readmissions using electronic medical records: Predictor importance across medical and surgical patient populations.

• Authors : Havranek, Michael M.; Hwang, Aljoscha B.; Funke, Ilona

Subjects: PATIENT readmissions; MACHINE learning; ELECTRONIC health recordsCENTERS for Medicare & Medicaid Services (U.S.)

• Source: PLoS ONE; 9/4/2025, Vol. 20 Issue 9, p1-17, 17p

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A Machine Learning-Based Assessment of Proxies and Drivers of Harmful Algal Blooms in the Western Lake Erie Basin Using Satellite Remote Sensing.

• Authors : Joshi, Neha; Ghoorkhanian, Armeen; Park, Jongmin

Subjects: ALGAL blooms; CHLOROPHYLL; WATER qualityLAKE Erie

• Source: Remote Sensing; Jul2025, Vol. 17 Issue 13, p2164, 24p

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Ensemble Machine Learning with R

• Authors : Tomislav Hengl; Carmelo Bonannella; Carmelo Bonannella

Subjects: point data; predictor variables; modeling

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Factors Associated with Tobacco Cessation Services Request Among Users of an Online Self-Screening Questionnaire.

• Authors : Hernández-Llanes, Norberto F.; Sánchez-Domínguez, Ricardo; Álvarez-Reza, Sofía

Subjects: SMOKING cessation; SELF-evaluation; RANDOM forest algorithmsMEXICO

• Source: Substance Use & Misuse; 2025, Vol. 60 Issue 4, p604-610, 7p

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Fine tuned CatBoost machine learning approach for early detection of cardiovascular disease through predictive modeling.

• Authors : Hamid, Muhammad; Hajjej, Fahima; Alluhaidan, Ala Saleh

Subjects: CARDIOVASCULAR diseases; MACHINE learning; FEATURE selection

• Source: Scientific Reports; 8/25/2025, Vol. 15 Issue 1, p1-15, 15p

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A machine learning approach for estimating forage maize yield and quality in NW Spain.

• Authors : García-Cortés, Silverio; Menéndez-Díaz, Agustín; Bande-Castro, María José

Subjects: CROP yields; CROP quality; BOOSTING algorithmsGALICIA (Spain : Region); NORTHERN Spain; ASTURIAS (Spain)

• Source: PLoS ONE; 8/12/2025, Vol. 20 Issue 8, p1-25, 25p

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