Prediction of bipolar disorder from mental episodes using machine learning approach

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.

Մատենագիտական մանրամասներ
Հիմնական հեղինակներ:	Tasmeem, Sumaiya, Rahaman, Motiur, Piasha, Karishma Meherin Khan, Yasar, Samin, Dina, Murshida Akter
Այլ հեղինակներ:	Rahman, Tanvir
Ձևաչափ:	Թեզիս
Լեզու:	English
Հրապարակվել է:	Brac University 2023
Խորագրեր:	Bipolar detection Random forest Machine learning Extreme gradient boosting Decision tree SVM MDQ Logistic regression CatBoost Light-GBM XGBoost Logistic regression analysis Deep learning (Machine learning)
Առցանց հասանելիություն:	http://hdl.handle.net/10361/21779

id	10361-21779
record_format	dspace
spelling	10361-217792023-10-12T21:02:21Z Prediction of bipolar disorder from mental episodes using machine learning approach Tasmeem, Sumaiya Rahaman, Motiur Piasha, Karishma Meherin Khan Yasar, Samin Dina, Murshida Akter Rahman, Tanvir Department of Computer Science and Engineering, Brac University Bipolar detection Random forest Machine learning Extreme gradient boosting Decision tree SVM MDQ Logistic regression CatBoost Light-GBM XGBoost Logistic regression analysis Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 48-49). A precious gift to mankind is the ability to express their emotions or feelings and also to realize. Sometimes, an omnipresent sustained feeling or emotion can dominate a person’s behavior and affect his perception which can also be defined as mood. There can be illnesses of mental health like any other diseases. A bipolar disorder is one of them which is also known as manic-depressive disorder where people feel overly happy and energized sometimes and feel very sad, hopeless and unmotivated other times. It can be thought of the highs and lows as two poles of mood and this is why it is named as bipolar disorder. There are many factors which work as the main reason for this disorder such as chemical imbalance in the brain, genetic issues, periods of high stress, over uses of drugs or alcohol and many others. Now-a-days cases of bipolar disorder are increasing at an alarming rate. If it can be predicted at the primary stage, the number of cases can be reduced. Technology plays a vital role in the health sector as it is used to lessen the complication and fasten the treatment. The aim of this research is to apply different Machine Learning algorithms to symptoms-based data of patients in order to help to build a model for prediction. This model will not only focus on detecting the disease but also will provide the primary treatment to the patient. We will develop a diagnostic algorithm based on an online questionnaire. Then, a trained dataset and machine learning algorithms will be used to recognize individual bipolar disorder patients. After that, to train and validate our diagnostic model we will use an extreme gradient boosting and cross validation. Another algorithm will be used which is called Light Gradient Boosting Machine Algorithm for ensuring the best result to fulfil our main goal. Last but not the least, some random forest algorithms will be used for detecting and differentiating between the types of BD accurately so that the cases of mistreatment can be brought down. Sumaiya Tasmeem Motiur Rahaman Samin Yasar Karishma Meherin Khan Piasha Murshida Akter Dina B.Sc. in Computer Science and Engineering 2023-10-12T04:00:46Z 2023-10-12T04:00:46Z ©2022 2022-06-05 Thesis ID 18101397 ID 17301210 ID 17301210 ID 17301101 ID 17241004 ID 18101233 http://hdl.handle.net/10361/21779 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. 58 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Bipolar detection Random forest Machine learning Extreme gradient boosting Decision tree SVM MDQ Logistic regression CatBoost Light-GBM XGBoost Logistic regression analysis Deep learning (Machine learning)
spellingShingle	Bipolar detection Random forest Machine learning Extreme gradient boosting Decision tree SVM MDQ Logistic regression CatBoost Light-GBM XGBoost Logistic regression analysis Deep learning (Machine learning) Tasmeem, Sumaiya Rahaman, Motiur Piasha, Karishma Meherin Khan Yasar, Samin Dina, Murshida Akter Prediction of bipolar disorder from mental episodes using machine learning approach
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
author2	Rahman, Tanvir
author_facet	Rahman, Tanvir Tasmeem, Sumaiya Rahaman, Motiur Piasha, Karishma Meherin Khan Yasar, Samin Dina, Murshida Akter
format	Thesis
author	Tasmeem, Sumaiya Rahaman, Motiur Piasha, Karishma Meherin Khan Yasar, Samin Dina, Murshida Akter
author_sort	Tasmeem, Sumaiya
title	Prediction of bipolar disorder from mental episodes using machine learning approach
title_short	Prediction of bipolar disorder from mental episodes using machine learning approach
title_full	Prediction of bipolar disorder from mental episodes using machine learning approach
title_fullStr	Prediction of bipolar disorder from mental episodes using machine learning approach
title_full_unstemmed	Prediction of bipolar disorder from mental episodes using machine learning approach
title_sort	prediction of bipolar disorder from mental episodes using machine learning approach
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/21779
work_keys_str_mv	AT tasmeemsumaiya predictionofbipolardisorderfrommentalepisodesusingmachinelearningapproach AT rahamanmotiur predictionofbipolardisorderfrommentalepisodesusingmachinelearningapproach AT piashakarishmameherinkhan predictionofbipolardisorderfrommentalepisodesusingmachinelearningapproach AT yasarsamin predictionofbipolardisorderfrommentalepisodesusingmachinelearningapproach AT dinamurshidaakter predictionofbipolardisorderfrommentalepisodesusingmachinelearningapproach
_version_	1814308019883737088

Prediction of bipolar disorder from mental episodes using machine learning approach

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