Atıf İçin Kopyala
Okyay S., Adar N.
PEERJ, cilt.10, sa.e13425, ss.1-19, 2022 (SCI-Expanded)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
10
Sayı:
e13425
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Basım Tarihi:
2022
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Doi Numarası:
10.7717/peerj.13425
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Dergi Adı:
PEERJ
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Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, CAB Abstracts, EMBASE, Veterinary Science Database, Directory of Open Access Journals
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Sayfa Sayıları:
ss.1-19
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Anahtar Kelimeler:
Cognitive Mining Learning, Clinical test scores, Dementia, Imputation, Incomplete data, Missing values, Reliability scale, User-based collaborative filtering, IMPUTATION, CLASSIFICATION
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Eskişehir Osmangazi Üniversitesi Adresli:
Evet
Özet
Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.