Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1519
Title: ChildPath: Diagnose depression in pre-schoolers based on daily activ
Authors: Kirthika, L.
Abeykoon, J.
Keywords: Pre-school depression
Depression status
CBCL
Machine Learning
Hidden Markov Model
Rule-based Machine learning
Issue Date: 10-Dec-2020
Publisher: 2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT
Series/Report no.: Vol.1;
Abstract: To determine depression in pre-schoolers and validation of identifying depression based on daily activities. A comprehensive literature search, interviews with accredited mental health practitioners and a survey was conducted to validate the background aspects and existing diagnosis theories to map out based on daily activities. The results of the evaluation suggest a gap around diagnosis of depression in pre-schoolers due to lack of awareness and its distinctive nature to adult depression. This establishes a need for depression status calculation mechanism based on analysis of daily activities using machine learning to examine behaviour and speech patterns. Further, rule-based machine learning, will be implemented to offer personalized treatment plans if diagnosed with a status of depression.
URI: http://rda.sliit.lk/handle/123456789/1519
ISBN: 978-1-7281-8412-8
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020

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