Abstract
Schizophrenia is a heterogeneous cognitive disorder where clinical classification is challenging because of the lack of well-established, non-invasive diagnoses biomarkers. There is, hence, a need for objective systems that can classify Schizophrenia despite challenges such as overlapping symptomatic factors, diverse internal clinical manifestations, and complex diagnostic process leading to delayed treatment. Thus, experimentation with automated machine learning architectural frameworks (AutoML) is presented in order to handle multimodal Functional Network Connectivity(FNC) and Source Based Morphometry(SBM) features based on functional magnetic resonance imaging(fMRI) and structural magnetic resonance imaging(sMRI) components respectively. On evaluating the resultant AutoML models with respect to approximately 280 machine learning architectures on the Overall AUC metric, the former outperforms the latter despite remarkable limitations including complex high dimensional feature space with very little data.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Accepted as poster contribution at Women in Machine Learning (WiML) workshop co-located with ICML 2020.
0 Accepted as workshop contribution at Women in Machine Learning (WiML) co-located with ICML 2020. Related code can be found at: https://github.com/GaganaB/AutoML
