Polycystic ovary syndrome detection using neural network.

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Bibliografiske detaljer
Main Authors:	Istiyaq, Tahsin, Jahan, Nusrat, Diptho, Rakib Ahmmed, Anika, Fairuz, Sadakin, Sifat-E
Andre forfattere:	Hossain, Muhammad Iqbal
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2024
Fag:	Machine learning KNN algorithm Linear regression analysis Regression analysis Polycystic ovary syndrome
Online adgang:	http://hdl.handle.net/10361/22861

id	10361-22861
record_format	dspace
spelling	10361-228612024-05-19T21:04:31Z Polycystic ovary syndrome detection using neural network. Istiyaq, Tahsin Jahan, Nusrat Diptho, Rakib Ahmmed Anika, Fairuz Sadakin, Sifat-E Hossain, Muhammad Iqbal Department of Computer Science and Engineering, Brac University Machine learning KNN algorithm Linear regression analysis Machine learning Regression analysis Polycystic ovary syndrome This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 29-30). A fairly frequent endocrine abnormality among women of reproductive age is polycystic ovary syndrome (PCOS). In this disease, the ovaries produce abnormally high levels of androgens, which are male sex hormones that are typically present in women in trace amounts. The basic difference between PCOS and normal ovarian cysts is the substantial hormonal imbalance, which is not a general occurrence in ovarian cysts. A study says that among 15 percent of reproductive women, this disease is found, which is a major cause of women’s infertility. Even though this is a very common and widely spread serious disease worldwide, it is hard to diagnose properly. So firstly, since this is a worldwide problem, a lot of people are thinking, but they cannot come to a conclusion. Secondly, detecting this disorder is very difficult since the symptoms of PCOS match those of other diseases, which makes detection difficult. For this reason, we became interested in this area. Tahsin Istiyaq Nusrat Jahan Sifat-E-Sadakin Rakib Ahmmed Diptho Fairuz Anika B.Sc in Computer Science and Engineering 2024-05-19T04:28:26Z 2024-05-19T04:28:26Z ©2023 2023-01 Thesis ID 19201111 ID 19201071 ID 18201179 ID 19201118 ID 20301464 http://hdl.handle.net/10361/22861 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. 34 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Machine learning KNN algorithm Linear regression analysis Machine learning Regression analysis Polycystic ovary syndrome
spellingShingle	Machine learning KNN algorithm Linear regression analysis Machine learning Regression analysis Polycystic ovary syndrome Istiyaq, Tahsin Jahan, Nusrat Diptho, Rakib Ahmmed Anika, Fairuz Sadakin, Sifat-E Polycystic ovary syndrome detection using neural network.
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Hossain, Muhammad Iqbal
author_facet	Hossain, Muhammad Iqbal Istiyaq, Tahsin Jahan, Nusrat Diptho, Rakib Ahmmed Anika, Fairuz Sadakin, Sifat-E
format	Thesis
author	Istiyaq, Tahsin Jahan, Nusrat Diptho, Rakib Ahmmed Anika, Fairuz Sadakin, Sifat-E
author_sort	Istiyaq, Tahsin
title	Polycystic ovary syndrome detection using neural network.
title_short	Polycystic ovary syndrome detection using neural network.
title_full	Polycystic ovary syndrome detection using neural network.
title_fullStr	Polycystic ovary syndrome detection using neural network.
title_full_unstemmed	Polycystic ovary syndrome detection using neural network.
title_sort	polycystic ovary syndrome detection using neural network.
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22861
work_keys_str_mv	AT istiyaqtahsin polycysticovarysyndromedetectionusingneuralnetwork AT jahannusrat polycysticovarysyndromedetectionusingneuralnetwork AT dipthorakibahmmed polycysticovarysyndromedetectionusingneuralnetwork AT anikafairuz polycysticovarysyndromedetectionusingneuralnetwork AT sadakinsifate polycysticovarysyndromedetectionusingneuralnetwork
_version_	1814309383720402944

Polycystic ovary syndrome detection using neural network.

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