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Academic Journal

Revolutionizing cardiovascular disease classification through machine learning and statistical methods.

Authors : Behera TK; Centre of Excellence in Natural Products and Therapeutics, Department of Biotechnology and Bioinformatics, Sambalpur University, Jyoti Vihar, Burla, Sambalpur, Odisha, India.; Sathia S

Subjects: Machine Learning*/Machine Learning*/Machine Learning*/statistics & numerical data ; Cardiovascular Diseases*/Cardiovascular Diseases*/Cardiovascular Diseases*/classification ; Cardiovascular Diseases*/Cardiovascular Diseases*/Cardiovascular Diseases*/diagnosis

Source: Journal of biopharmaceutical statistics [J Biopharm Stat] 2026 May; Vol. 36 (3), pp. 504-526. Date of Electronic Publication: 2024 Nov 24.Publisher: Taylor & Francis Country of Publication: England NLM ID: 9200436 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Academic Journal

Investigating the predictive power of seismic statistical features using ensemble learning.

Authors : Quan W; Department of Computer Science, University College London, London, United Kingdom.; Gorse D

Subjects: Earthquakes*/Earthquakes*/Earthquakes*/statistics & numerical data ; Machine Learning* ; Models, Statistical*

Source: PloS one [PLoS One] 2026 Feb 19; Vol. 21 (2), pp. e0342765. Date of Electronic Publication: 2026 Feb 19 (Print Publication: 2026).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.

Authors : Lu Y; School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.; Chen C

Subjects: Renal Dialysis*/Renal Dialysis*/Renal Dialysis*/adverse effects ; Cardiovascular Diseases*/Cardiovascular Diseases*/Cardiovascular Diseases*/etiology ; Cardiovascular Diseases*/Cardiovascular Diseases*/Cardiovascular Diseases*/diagnosis

Source: Renal failure [Ren Fail] 2025 Dec; Vol. 47 (1), pp. 2587490. Date of Electronic Publication: 2025 Nov 19.Publisher: Informa Healthcare Country of Publication: England NLM ID: 8701128 Publication Model: Print-Electronic Cited Medium: Internet ISSN:

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Academic Journal

Gene expression and metadata based identification of key genes for lung cancer, COPD, and IPF using machine learning and statistical models.

Authors : Yasmin MF; Department of Computing and Information System(CIS), Daffodil International University (DIU), Ashulia, Dhaka, Bangladesh.; Hosen MF

Subjects: Pulmonary Disease, Chronic Obstructive*/Pulmonary Disease, Chronic Obstructive*/Pulmonary Disease, Chronic Obstructive*/genetics ; Lung Neoplasms*/Lung Neoplasms*/Lung Neoplasms*/genetics ; Idiopathic Pulmonary Fibrosis*/Idiopathic Pulmonary Fibrosis*/Idiopathic Pulmonary Fibrosis*/genetics

Source: PloS one [PLoS One] 2026 Mar 19; Vol. 21 (3), pp. e0344666. Date of Electronic Publication: 2026 Mar 19 (Print Publication: 2026).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.

Authors : Jahani S; Roshanaei G ...

Subjects: Alzheimer Disease*/Alzheimer Disease*/Alzheimer Disease*/diagnosis ; Alzheimer Disease*/Alzheimer Disease*/Alzheimer Disease*/diagnostic imaging ; Cognitive Dysfunction*/Cognitive Dysfunction*/Cognitive Dysfunction*/diagnosis

Source: BMC medical research methodology [BMC Med Res Methodol] 2025 Nov 05; Vol. 25 (1), pp. 250. Date of Electronic Publication: 2025 Nov 05.Publisher: BioMed Central Country of Publication: England NLM ID: 100968545 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2288

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Academic Journal

A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh.

Authors : Hossain MS; Department of Industrial Engineering and Management, Khulna University of Engineering & Technology, Khulna, Bangladesh.; Parvin F

Subjects: Restaurants*/Restaurants*/Restaurants*/statistics & numerical data ; Restaurants*/Restaurants*/Restaurants*/economics ; Machine Learning*

Source: PloS one [PLoS One] 2025 Jun 04; Vol. 20 (6), pp. e0325449. Date of Electronic Publication: 2025 Jun 04 (Print Publication: 2025).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.

Authors : Zhou Y; School of Computing and Data Science, The University of Hong Kong, Hong Kong Special Administration Region, China.; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administration Region, China.

Subjects: Cardiovascular Diseases*/Cardiovascular Diseases*/Cardiovascular Diseases*/prevention & control ; Cardiovascular Diseases*/Cardiovascular Diseases*/Cardiovascular Diseases*/epidemiology ; Primary Prevention*/Primary Prevention*/Primary Prevention*/methods

Source: PloS one [PLoS One] 2025 Jul 28; Vol. 20 (7), pp. e0322419. Date of Electronic Publication: 2025 Jul 28 (Print Publication: 2025).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models.

Authors : Almarashi AM; Department of Statistics, Faculty of Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia. .

Subjects: HIV Infections*/HIV Infections*/HIV Infections*/drug therapy ; HIV Infections*/HIV Infections*/HIV Infections*/epidemiology ; Anti-Retroviral Agents*/Anti-Retroviral Agents*/Anti-Retroviral Agents*/therapeutic use

Source: Scientific reports [Sci Rep] 2025 Jul 25; Vol. 15 (1), pp. 27033. Date of Electronic Publication: 2025 Jul 25.Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN:

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Academic Journal

Statistical and machine learning models for predicting university dropout and scholarship impact.

Authors : Romero S; Department of Mathematics and Statistics, San Diego State University, San Diego, California, United States of America.; Liao X

Subjects: Student Dropouts*/Student Dropouts*/Student Dropouts*/statistics & numerical data ; Fellowships and Scholarships*/Fellowships and Scholarships*/Fellowships and Scholarships*/statistics & numerical data ; Machine Learning*

Source: PloS one [PLoS One] 2025 Jun 25; Vol. 20 (6), pp. e0325047. Date of Electronic Publication: 2025 Jun 25 (Print Publication: 2025).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Academic Journal

A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction.

Authors : Khatun MR; Department of Computer Science and Engineering, Bangladesh University, Dhaka, Bangladesh.; Mim MA

Subjects: Student Dropouts*/Student Dropouts*/Student Dropouts*/statistics & numerical data ; Machine Learning*; Humans

Source: PloS one [PLoS One] 2025 Sep 10; Vol. 20 (9), pp. e0331917. Date of Electronic Publication: 2025 Sep 10 (Print Publication: 2025).Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet

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Revolutionizing cardiovascular disease classification through machine learning and statistical methods.

Investigating the predictive power of seismic statistical features using ensemble learning.

Systematic review and comparison of machine learning and conventional statistical models for predicting cardiovascular events in dialysis patients.

Gene expression and metadata based identification of key genes for lung cancer, COPD, and IPF using machine learning and statistical models.

Assessing the accuracy of survival machine learning and traditional statistical models for Alzheimer's disease prediction over time: a study on the ADNI cohort.

A comparative study of various statistical and machine learning models for predicting restaurant demand in Bangladesh.

Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learning approach.

Statistical modelling and forecasting of HIV and anti-retroviral therapy cases by time-series and machine learning models.

Statistical and machine learning models for predicting university dropout and scholarship impact.

A hybrid framework of statistical, machine learning, and explainable AI methods for school dropout prediction.

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