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22 July 2024
Reseach Article

Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification

by Tauseef Ibne Mamun, Lamia Alam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 51
Year of Publication: 2022
Authors: Tauseef Ibne Mamun, Lamia Alam

Tauseef Ibne Mamun, Lamia Alam . Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification. International Journal of Computer Applications. 183, 51 ( Feb 2022), 27-32. DOI=10.5120/ijca2022921924

@article{ 10.5120/ijca2022921924,
author = { Tauseef Ibne Mamun, Lamia Alam },
title = { Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2022 },
volume = { 183 },
number = { 51 },
month = { Feb },
year = { 2022 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2022921924 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:15:30.683272+05:30
%A Tauseef Ibne Mamun
%A Lamia Alam
%T Predicting Depression using a Biochemistry Profile and Machine Learning for Better Risk Stratification
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 51
%P 27-32
%D 2022
%I Foundation of Computer Science (FCS), NY, USA

Depression is one of the mental health issues that are responsible for various morbidities if it remains undiagnosed. Previous works that used machine learning to predict depression used mainly qualitative information like socio-economic or socio-demographic data for creating predictive models. But there is a chance of getting biased qualitative data from patients that will hinder any correct prediction. Our goal was to examine if the major form of depression can be predicted with a high accuracy using a patient’s biochemistry profile using Machine Learning. We also examined if minor depression can be predicted for a large portion of patients. The result suggests the method we proposed can be used as the first point of screening for depression especially major depression.

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Index Terms

Computer Science
Information Sciences


Depression Early Detection Risk Stratification