Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1519
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dc.contributor.authorKirthika, L.-
dc.contributor.authorAbeykoon, J.-
dc.date.accessioned2022-03-07T06:57:11Z-
dc.date.available2022-03-07T06:57:11Z-
dc.date.issued2020-12-10-
dc.identifier.isbn978-1-7281-8412-8-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1519-
dc.description.abstractTo 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.en_US
dc.language.isoenen_US
dc.publisher2020 2nd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.relation.ispartofseriesVol.1;-
dc.subjectPre-school depressionen_US
dc.subjectDepression statusen_US
dc.subjectCBCLen_US
dc.subjectMachine Learningen_US
dc.subjectHidden Markov Modelen_US
dc.subjectRule-based Machine learningen_US
dc.titleChildPath: Diagnose depression in pre-schoolers based on daily activen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICAC51239.2020.9357230en_US
Appears in Collections:2nd International Conference on Advancements in Computing (ICAC) | 2020

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