skip to main content
Idioma:

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

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

Automatika, 2021-01, Vol.62 (1), p.109-117 [Periódico revisado por pares]

Ljubljana: Taylor & Francis

Texto completo disponível

Citações Citado por
  • Título:
    Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females
  • Autor: Bozek, Jelena ; Kesedzic, Ivan ; Novosel, Leonard ; Bozek, Tomislav
  • Assuntos: Algorithms ; Brain ; brain MRI ; Classification ; Classifiers ; Cognitive tasks ; Datasets ; features ; Females ; Gender aspects ; Gender differences ; HCP ; Machine learning ; Magnetic resonance imaging ; Males ; Sex ; sex differences ; Support vector machines
  • É parte de: Automatika, 2021-01, Vol.62 (1), p.109-117
  • Descrição: 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.
  • Editor: Ljubljana: Taylor & Francis
  • Idioma: Inglês;Croatian
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  4 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.