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 |
Files in This Item:
File | Description | Size | Format | |
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ChildPath_Diagnose_depression_in_pre-schoolers_based_on_daily_activities.pdf Until 2050-12-31 | 497.54 kB | Adobe PDF | View/Open Request a copy |
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