Polycystic Ovarian Syndrome (PCOS) Classification and Feature Selection by Machine Learning Techniques

Authors

  • Satish C. R Nandipati School of Computer Sciences, 11800, Universiti Sains Malaysia, Pulau Pinang, Malaysia.
  • Chew Xin Ying School of Computer Sciences, 11800, Universiti Sains Malaysia, Pulau Pinang, Malaysia

Abstract

Mathematics anxiety or mathematics phobia is a general term for several disorders that will cause panic attacks, nervousness, and social anxiety, which potentially gave negative outcomes when faced with any situation related to mathematical problems. Therefore, the purpose of this study is to examine the factors that influence mathematics anxiety among private college students. Two private colleges from north Malaysia were selected from its nine branches all over Malaysia. These colleges offered 9 different courses where each course offered mathematics subject. After conducting factor analysis, five factors were identified as reasons for the occurrence of mathematics anxiety among students. The factors are student attitude, role of teacher, skills, emotions, and peers. Structural equation model has shown that there are relationships between these five factors. Peers and role of teachers proven to have a positive direct effect on mathematics anxiety with role of teachers was found to be the strongest factor. Meanwhile, students’ attitude and skills have a negative direct effect on mathematics anxiety. Finally, emotions influence mathematics anxiety indirectly through students’ attitude.

Keywords:

Classification, Feature selection, PCOS, Python-Scikit learn package, RapidMiner

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Published

2020-12-31

How to Cite

Satish C. R Nandipati, & Chew Xin Ying. (2020). Polycystic Ovarian Syndrome (PCOS) Classification and Feature Selection by Machine Learning Techniques. Applied Mathematics and Computational Intelligence (AMCI), 9, 65–74. Retrieved from https://ejournal.unimap.edu.my/index.php/amci/article/view/151

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Articles