EMPOWERING SELF-DETECTION: A GRAPHICAL USER INTERFACE POWERED BY MACHINE LEARNING FOR EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE

Basit Öğe Kaydı
dc.contributor.authorKabakus, Abdullah Talha
dc.contributor.authorErdoğmuş, Pakize
dc.date.accessioned2025-10-11T20:37:53Z
dc.date.available2025-10-11T20:37:53Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractAlzheimer’s Disease (AD) is one of the most, if not the most, devastating neurodegenerative diseases that are incurable and progressive. Early diagnosis of AD comes with many promises in terms of medicine, sociology, and economics. Despite the existence of numerous studies that aim for early diagnosis of AD, to the best of our knowledge, there is not a publicly available tool that lets end-users assess AD. To address this gap, we propose a Graphical User Interface (GUI) powered by Machine Learning (ML) that makes self-assessment of AD possible – without any input from medical experts. The developed GUI lets end-users enter various information considering both commonly used features for the diagnosis of AD and the questions available in the gold standard screening tool for the diagnosis of AD, namely the Mini-Mental State Exam. In addition to employing 11 traditional ML algorithms, we propose a novel 1-dimensional (1D) Convolutional Neural Network (CNN). All ML models were trained on a gold standard dataset that comprised 373 records from three subject classes as follows: (i) non- demented, (ii) demented, and (iii) converted. Once the end- user enters the required input through the developed GUI, the previously trained ML model assesses the diagnosis of AD through this input in a couple of seconds. According to the experimental results, the proposed novel 1D CNN outperformed the state-of-the-art by obtaining an accuracy as high as 95,3% on the used gold standard dataset.en_US
dc.identifier.doi10.55071/ticaretfbd.416508
dc.identifier.endpage270en_US
dc.identifier.issn1305-7820
dc.identifier.issn2587-165X
dc.identifier.issue46en_US
dc.identifier.startpage245en_US
dc.identifier.trdizinid1287759en_US
dc.identifier.urihttps://doi.org/10.55071/ticaretfbd.416508
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1287759
dc.identifier.urihttps://hdl.handle.net/20.500.12684/20732
dc.identifier.volume23en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofİstanbul Ticaret Üniversitesi Fen Bilimleri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_TR_20250911
dc.subjectMachine learningen_US
dc.subjectdeep learningen_US
dc.subjectclassificationen_US
dc.subjectcognitive disorderen_US
dc.subjectdementiaen_US
dc.subjectAlzheimer’s Diseaseen_US
dc.titleEMPOWERING SELF-DETECTION: A GRAPHICAL USER INTERFACE POWERED BY MACHINE LEARNING FOR EARLY DIAGNOSIS OF ALZHEIMER'S DISEASEen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu