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All Journal International Journal of Advances in Intelligent Informatics
Alanoud Alqifari
College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University
Computer Science & IT

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Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis Zuhaira Muhammad Zain; Mona Alshenaifi; Abeer Aljaloud; Tamadhur Albednah; Reham Alghanim; Alanoud Alqifari; Amal Alqahtani
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.462

Abstract

Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.
Co-Authors Abeer Aljaloud Amal Alqahtani Mona Alshenaifi Reham Alghanim Tamadhur Albednah Zuhaira Muhammad Zain