Predicting depression in Bangladeshi undergraduates using machine learning

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.

Detalles Bibliográficos
Main Authors:	Choudhury, Ahnaf Atef, Khan, Md Rezwan Hassan, Nahim, Nabuat Zaman, Tulon, Sadid Rafsun
Outros autores:	Chakrabarty, Amitabha
Formato:	Thesis
Idioma:	English
Publicado:	BRAC University 2019
Subjects:	Depression Machine learning Undergraduates Bangladeshi
Acceso en liña:	http://hdl.handle.net/10361/11424

id	10361-11424
record_format	dspace
spelling	10361-114242022-01-26T10:08:24Z Predicting depression in Bangladeshi undergraduates using machine learning Choudhury, Ahnaf Atef Khan, Md Rezwan Hassan Nahim, Nabuat Zaman Tulon, Sadid Rafsun Chakrabarty, Amitabha Department of Computer Science and Engineering, BRAC University Depression Machine learning Undergraduates Bangladeshi Machine learning This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Includes bibliographical references (pages 75-80). Cataloged from PDF version of thesis. Depression is a major disorder and a growing problem that impacts a person’s way of living, disrupting natural functioning and impeding thought processes while they might remain oblivious to the fact that they are suffering from such a disease. Depression is especially prevalent in the younger population of underdeveloped and developing countries. Youth in countries such as Bangladesh face difficulties with studies, jobs, relationships, drugs, family problems which are all major or minor contributors in a pathway to depression. Furthermore, people in Bangladesh are not comfortable in speaking about this illness and often misinterpret this disorder as madness. This research besides predicting depression in university undergraduates for the purpose of recommendation to a psychiatric, focuses on gaining valuable insights as to why university students of Bangladesh, undergraduates in particular suffer from depression. The data for this research was collected by a survey designed after consultation with psychologists, counsellors and professors. The survey was carried out through paper and Google survey form. The data was analyzed to find out relevant features related to depression using Random Forest Algorithm and then predict depression based on those features. A best method for predicting depression among Bangladesh undergraduates was found out after using six algorithms to train and test the dataset. Deep Learning was found to be the best algorithm with the lowest number of false negatives, closely followed by Gradient Boost Algorithm both with an F-Measure of 63%. Generalized Linear Model, Random Forest, K-Nearest Neighbor and Support Vector Machine were the other four algorithms used for comparison. The objective of this research is to determine reasons for depression and to check whether depression can be successfully predicted with the help of related features. Depression is an illness that people in Bangladesh tend to ignore and hence it builds up and worsens with time. This research aims to identify depression in its early stages and ensure a fast recovery for victims so that heartbreaking incidents like suicide can be avoided. Ahnaf Atef Choudhury Md Rezwan Hassan Khan Nabuat Zaman Nahim Sadid Rafsun Tulon B. Computer Science and Engineering 2019-02-17T09:28:36Z 2019-02-17T09:28:36Z 2018 2018-12 Thesis ID 15101022 ID 15101078 ID 15301084 ID 15101005 http://hdl.handle.net/10361/11424 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 80 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Depression Machine learning Undergraduates Bangladeshi Machine learning
spellingShingle	Depression Machine learning Undergraduates Bangladeshi Machine learning Choudhury, Ahnaf Atef Khan, Md Rezwan Hassan Nahim, Nabuat Zaman Tulon, Sadid Rafsun Predicting depression in Bangladeshi undergraduates using machine learning
description	This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Choudhury, Ahnaf Atef Khan, Md Rezwan Hassan Nahim, Nabuat Zaman Tulon, Sadid Rafsun
format	Thesis
author	Choudhury, Ahnaf Atef Khan, Md Rezwan Hassan Nahim, Nabuat Zaman Tulon, Sadid Rafsun
author_sort	Choudhury, Ahnaf Atef
title	Predicting depression in Bangladeshi undergraduates using machine learning
title_short	Predicting depression in Bangladeshi undergraduates using machine learning
title_full	Predicting depression in Bangladeshi undergraduates using machine learning
title_fullStr	Predicting depression in Bangladeshi undergraduates using machine learning
title_full_unstemmed	Predicting depression in Bangladeshi undergraduates using machine learning
title_sort	predicting depression in bangladeshi undergraduates using machine learning
publisher	BRAC University
publishDate	2019
url	http://hdl.handle.net/10361/11424
work_keys_str_mv	AT choudhuryahnafatef predictingdepressioninbangladeshiundergraduatesusingmachinelearning AT khanmdrezwanhassan predictingdepressioninbangladeshiundergraduatesusingmachinelearning AT nahimnabuatzaman predictingdepressioninbangladeshiundergraduatesusingmachinelearning AT tulonsadidrafsun predictingdepressioninbangladeshiundergraduatesusingmachinelearning
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Predicting depression in Bangladeshi undergraduates using machine learning

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