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Pregled bibliografske jedinice broj: 1111122

Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females


Bozek, Jelena; Kesedzic, Ivan; Novosel, Leonard; Bozek, Tomislav
Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females // Automatika, 62 (2021), 1; 109-117 doi:10.1080/00051144.2021.1885890 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1111122 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females

Autori
Bozek, Jelena ; Kesedzic, Ivan ; Novosel, Leonard ; Bozek, Tomislav

Izvornik
Automatika (0005-1144) 62 (2021), 1; 109-117

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
sex differences ; features ; classification ; HCP ; brain MRI ; machine learning

Sažetak
We analysed features relevant for differentiation between males and females based on the data available from the Human Connectome Project (HCP) S1200 dataset. We used 354 features containing cognitive and emotional measures as well as measures derived from task functional magnetic resonance imaging (MRI) and structural brain MRI. The paper presents a thorough analysis of this extensive set of features using a machine learning approach with a goal of identifying features that have the ability to differentiate between males and females. We used two state of the art classification algorithms with different properties: support vector machine (SVM) and random forest classifier (RFC). For each classifier the hyperparameters were obtained and classifiers were optimized using nested cross validation and grid search. This resulted in the classification performance of 91% and 89% accuracy using SVM and RFC, respectively. Using SHAP (SHapley Additive exPlanations) method we obtained relevance of features as indicators of sex differences and identified features with high discriminative power for sex classification. The majority of top features were brain morphological measures, and only a small proportion were features related to cognitive performance. Our results demonstrate the importance and advantages of using a machine learning approach when analysing sex differences.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne medicinske znanosti



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Klinika za dijabetes, endokrinologiju i bolesti metabolizma Vuk Vrhovac,
Medicinski fakultet, Zagreb

Profili:

Avatar Url Ivan Kesedžić (autor)

Avatar Url Leonard Novosel (autor)

Avatar Url Jelena Božek (autor)

Poveznice na cjeloviti tekst rada:

doi www.tandfonline.com

Citiraj ovu publikaciju:

Bozek, Jelena; Kesedzic, Ivan; Novosel, Leonard; Bozek, Tomislav
Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females // Automatika, 62 (2021), 1; 109-117 doi:10.1080/00051144.2021.1885890 (međunarodna recenzija, članak, znanstveni)
Bozek, J., Kesedzic, I., Novosel, L. & Bozek, T. (2021) Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females. Automatika, 62 (1), 109-117 doi:10.1080/00051144.2021.1885890.
@article{article, author = {Bozek, Jelena and Kesedzic, Ivan and Novosel, Leonard and Bozek, Tomislav}, year = {2021}, pages = {109-117}, DOI = {10.1080/00051144.2021.1885890}, keywords = {sex differences, features, classification, HCP, brain MRI, machine learning}, journal = {Automatika}, doi = {10.1080/00051144.2021.1885890}, volume = {62}, number = {1}, issn = {0005-1144}, title = {Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females}, keyword = {sex differences, features, classification, HCP, brain MRI, machine learning} }
@article{article, author = {Bozek, Jelena and Kesedzic, Ivan and Novosel, Leonard and Bozek, Tomislav}, year = {2021}, pages = {109-117}, DOI = {10.1080/00051144.2021.1885890}, keywords = {sex differences, features, classification, HCP, brain MRI, machine learning}, journal = {Automatika}, doi = {10.1080/00051144.2021.1885890}, volume = {62}, number = {1}, issn = {0005-1144}, title = {Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females}, keyword = {sex differences, features, classification, HCP, brain MRI, machine learning} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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