Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques

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Main Authors:	Satish, C. R Nandipati, Chew, XinYing, Khaw, Khai Wah
Other Authors:	xinying@usm.my
Format:	Article
Language:	English
Published:	Institute of Engineering Mathematics, Universiti Malaysia Perlis 2021
Subjects:	Classification Feature selection PCOS Python-Scikit learn package RapidMiner
Online Access:	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69392
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-69392
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spelling	my.unimap-693922021-01-26T07:47:51Z Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques Satish, C. R Nandipati Chew, XinYing Khaw, Khai Wah xinying@usm.my Classification Feature selection PCOS Python-Scikit learn package RapidMiner Link to publisher's homepage at https://amci.unimap.edu.my/ One of the most common endocrine system disorders which affect about 5 to 10 % of the adolescent women is Polycystic Ovarian Syndrome (PCOS). The symptoms include failure to ovulate and infertility, cardiovascular diseases, type 2 diabetes, etc. The detection of PCOS can be done through biochemical, clinical and ultrasonography methods. It is known that early diagnosis and treatment could reduce the chance of PCOS. Hence, it is necessary to know which classification model and features play a significant role in the prediction of disease, which is the objective of this study with Python-Scikit Learn package and RapidMiner. Despite different tools used, the highest accuracy is shown by Random Forest (93.12%, RapidMiner) with the complete dataset. On the other hand, KNN and SVM show similar accuracy performances (90.83%, RapidMiner) with 10 selected features. The average performances of 10 and 24 selected features show insignificance and significance with the combined dataset, indicating these features could be used and cannot be used for the prediction of PCOS, respectively. A comparison of both tools and their performances shows that the RapidMiner performs better than Python. However, it depends on the performance of the classification model which in turn dependent on the nature of the dataset and techniques used. 2021-01-26T07:47:51Z 2021-01-26T07:47:51Z 2020-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.9, 2020, pages 65-74 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69392 en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Classification Feature selection PCOS Python-Scikit learn package RapidMiner
spellingShingle	Classification Feature selection PCOS Python-Scikit learn package RapidMiner Satish, C. R Nandipati Chew, XinYing Khaw, Khai Wah Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques
description	Link to publisher's homepage at https://amci.unimap.edu.my/
author2	xinying@usm.my
author_facet	xinying@usm.my Satish, C. R Nandipati Chew, XinYing Khaw, Khai Wah
format	Article
author	Satish, C. R Nandipati Chew, XinYing Khaw, Khai Wah
author_sort	Satish, C. R Nandipati
title	Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques
title_short	Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques
title_full	Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques
title_fullStr	Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques
title_full_unstemmed	Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques
title_sort	polycystic ovarian syndrome (pcos) classification and feature selection by machine learning techniques
publisher	Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate	2021
url	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/69392
_version_	1698698597800869888

Polycystic Ovarian Syndrome (PCOS) classification and feature selection by machine learning techniques

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