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Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review.

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Title: Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review.
Authors: Dhanka, Sanjay1, sanjaykumar506070@gmail.com, Sharma, Abhinav1, abhinav.engi@gmail.com, Kumar, Ankur2, khatriankur007@yahoo.com, Maini, Surita1, suritamaini@gmail.com, Vundavilli, Haswanth3, haswanth@iitism.ac.in
Source: Archives of Computational Methods in Engineering; Jan2026, Vol. 33 Issue 1, p289-324, 36p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191451277
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review.
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  Data: <searchLink fieldCode="JN" term="%22Archives+of+Computational+Methods+in+Engineering%22">Archives of Computational Methods in Engineering</searchLink>; Jan2026, Vol. 33 Issue 1, p289-324, 36p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191451277
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      – Type: doi
        Value: 10.1007/s11831-025-10309-5
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      – Code: eng
        Text: English
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        PageCount: 36
        StartPage: 289
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      – TitleFull: Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review.
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            NameFull: Dhanka, Sanjay
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            NameFull: Sharma, Abhinav
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            NameFull: Kumar, Ankur
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            NameFull: Maini, Surita
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            – D: 01
              M: 01
              Text: Jan2026
              Type: published
              Y: 2026
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              Value: 33
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