Advanced hybrid and preprocessing models for diagnosis challenges in data classification

Basit Öğe Kaydı
dc.contributor.authorFayez, Mustafa Adil
dc.contributor.authorKurnaz, Sefer
dc.date.accessioned2024-12-05T06:53:08Z
dc.date.available2024-12-05T06:53:08Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractMachine Learning (ML), often viewed as a cutting-edge technology best suited for qualified specialists, presents limited access for other physicians and scientists in the medical profession. In this work, we provide a new, sophisticated, and highly successful technology for medical applications, especially cardiac diagnostics. We propose a novel advanced hybrid optimization model with two essential parts. Initially, we apply a high-performance hybrid resampling technique for feature engineering and pre-processing. This approach, which combines Synthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN) with Neighborhood Cleaning Rules (NCL), addresses class imbalance in the data. We developed a complex hybrid optimization model that incorporates hyper-parameter optimization, advanced Application Programming Interface (API) functions, and a super-learner ensemble model to enhance diagnosis accuracy in cases where datasets lack balance. Furthermore, we developed high-performance prediction models using sophisticated Support Vector Machines (SVMs). We show that, with re-sampled Cardiovascular Disease (CVD) data, the advanced hybrid optimization model attained an astounding accuracy of 98%. By comparison, an advanced SVM model obtained 96% accuracy, while an advanced deep learning model produced 95.5% accuracy. Our new sophisticated hybrid optimization machine learning models may significantly improve physicians’ interpretation of ML results. This strategy could make it easier to apply AI methods on a large scale in the clinic, which would eventually raise patient outcomes and diagnostic accuracy.en_US
dc.identifier.citationFayez, M. A., Kurnaz, S. (2024). Advanced hybrid and preprocessing models for diagnosis challenges in data classification. Journal of Advances in Information Technology, 12(11), 1264-1272. 10.12720/jait.15.11.1264-1272en_US
dc.identifier.endpage1272en_US
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85210263799
dc.identifier.scopusqualityQ2
dc.identifier.startpage1264en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5075
dc.identifier.volume15en_US
dc.identifier.wosWOS:001374446300002
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorFayez, Mustafa Adil
dc.institutionauthorKurnaz, Sefer
dc.language.isoen
dc.publisherEngineering and Technology Publishingen_US
dc.relation.ispartofJournal of Advances in Information Technology
dc.relation.isversionof10.12720/jait.15.11.1264-1272en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectApplication Programming Interface (API) functionen_US
dc.subjectCardiovascular Disease (CVD)en_US
dc.subjectHybrid advanced modelsen_US
dc.subjectNeighborhood Cleaning Rules (NCL)en_US
dc.subjectOptimizationen_US
dc.subjectSynthetic Minority Oversampling Technique Edited Nearest Neighbors (SMOTEENN)en_US
dc.titleAdvanced hybrid and preprocessing models for diagnosis challenges in data classification
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
JAIT-V15N11-1264.pdf
Boyut:
1.92 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu