Abstract
Machine learning (ML) techniques are crucial for improving diagnostic accuracy in psychiatry using neuroimaging-based biomarkers. Deep learning models like Kolmogorov Arnold Networks (KANs) are particularly promising in this context but struggle with high-dimensional datasets. We propose the Ensemble-KAN (E-KAN) method to overcome these limitations, integrating multiple base learners. Our novel approach aims to advance classification especially when multiple sources of data are available. The E-KAN was tested against traditional ML models in discriminating recent-onset psychosis (ROP) or depression (ROD) from healthy controls using multimodal environmental and neuroimaging data and it underwent a rigorous ablation study to test its effectiveness. Results demonstrate enhanced performance over traditional ML models, highlighting the efficacy of E-KAN models in psychiatric diagnostics. Specifically, our E-KAN achieved an accuracy of 72.5%, outperforming single-KAN models and traditional ML algorithms. This study underscores the potential of E-KAN models in advancing psychiatric research and personalized medicine through improved diagnostic capabilities. The code is available at https://github.com/brainpolislab/E-KAN.
N. Koutsouleris, P. Brambilla and E. Maggioni—These authors contributed equally to this work.
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Acknowledgments
This study was supported by EU-FP7 project PRONIA (Personalized Prognostic Tools for Early Psychosis Management) under the Grant Agreement n\(^{\textrm{o}}\) 602152 (PI: NK). EM was supported by the European Union - NextGeneration EU (PRIN 2022 PNRR, grant n. P20229MFRC); GDF was supported by the Italian Ministry of Health (grant n\(^{\textrm{o}}\) GR-2018-12367290). IWS was supported by grants from EBRAINS-Italy, project funded under the National Recovery and Resilience Plan (NRRP), Mission 4, “Education and Research” - Component 2, “From research to Business” Investment 3.1 - Call for tender n\(^{\textrm{o}}\) 3264 of Dec 28, 2021 of Italian Ministry of University and Research (MUR) funded by the European Union - NextGenerationEU, with award number: Project code IR0000011, Concession Decree n\(^{\textrm{o}}\) 117 of June 21, 2022 adopted by the Italian Ministry of University and Research, CUP B51E22000150006, Project title “EBRAINS-Italy (European Brain ReseArch INfrastruc-tureS-Italy). PB was partially supported by the Italian Ministry of University and Research (Dipartimenti di Eccellenza Program 2023-2027 - Dept of Pathophysiology and Transplantation, University of Milan), the Italian Ministry of Health (Hub Life Science- Diagnostica Avanzata, HLS-DA, PNC-E3-2022-23683266- CUP: C43C22001630001/MI-0117; Ricerca Corrente 2024) and by the Fondazione Cariplo (grant n\(^{\textrm{o}}\) 2019-3416).
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De Franceschi, G. et al. (2025). Ensemble-KAN: Leveraging Kolmogorov Arnold Networks to Discriminate Individuals with Psychiatric Disorders from Controls. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2024. Lecture Notes in Computer Science, vol 15384. Springer, Cham. https://doi.org/10.1007/978-3-031-82007-6_18
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