Title: An actigraph data-based early diagnosis of depression using ensemble classifiers

Authors: C.D. Anisha; N. Arulanand; R. Rekha

Addresses: Department of CSE, PSGCT, Coimbatore, India ' Department of CSE, PSGCT, Coimbatore, India ' Department of IT, PSGCT, Coimbatore, India

Abstract: Depression is one of the severe mental disorders which prevails as one of the key symptoms in unipolar and bipolar disorder. An early diagnosis of depression can lead to quicker recovery. This paper proposes an artificial intelligence (AI)-based early diagnosis system for depression using the actigraph motor data. The key contribution of the paper is the 'ensemble classifiers' which is a type of machine learning (ML) model, a subpart of AI model, which improves the diagnosis of depression state by combining the predictions of various single classifiers. The result signifies that the proposed system with ensemble classifier AI model has an accuracy of 85% which is reliable and consistent than existing systems.

Keywords: bipolar disorder; depression; actigraph; ensemble classifiers.

DOI: 10.1504/IJMEI.2024.138284

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.3, pp.252 - 259

Received: 04 Oct 2021
Accepted: 08 Feb 2022
Published online: 01 May 2024 *

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