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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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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
score 13.222552

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