Abstract
Major Depressive Disorder (MDD) is a most prevalent psychiatric disease which causes functional disabilities resulting in social problems. There is structural and functional impairments of some core brain regions in patients with MDD which results in the spontaneous fluctuation in neural blood oxygen-level dependent (BOLD) signal at resting state. This is measured by using resting-state functional Magnetic Resonance Imaging (rs-fMRI) tool to predict MDD. The impact of technological development is increasing in medicine and as well in every branch of science and the analysis of medical data by latest technology is also increasing. So the exact characterization of rs-fMRI is shown by Supervised Machine learning approaches, often in a data-driven manner which is used for accurate prediction of MDD. In this study, a different distance metrics is evaluated for K-Nearest Neighbor (KNN) algorithm and to improve the performance of the algorithm various tuning parameters are used for effective prediction of MDD patients from healthy controls.
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Saranya, S., Kavitha, N. (2022). Prognosis of Clinical Depression with Resting State Functionality Connectivity using Machine Learning. In: Unhelker, B., Pandey, H.M., Raj, G. (eds) Applications of Artificial Intelligence and Machine Learning. Lecture Notes in Electrical Engineering, vol 925. Springer, Singapore. https://doi.org/10.1007/978-981-19-4831-2_29
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DOI: https://doi.org/10.1007/978-981-19-4831-2_29
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