Development of Statistically Modelled Feature Selection Method for Microwave Breast Cancer Detection

Authors

  • V. Vijayasarveswari Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Norfadila Mahrom Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Rafikha Aliana A. Raof Sports Engineering Research Centre (SERC), Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Len Al Eh Kan Phak Advanced Computing, Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Muhammad Amiruddin Ab Razak Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Bavanraj Punniya Silan Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Ahmad Ashraf Abdul Halim Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Mohd Wafi Nasrudin Advanced Computing, Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Nuraminah Ramli Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia
  • Yusnita Rahayu Department of Electrical Engineering, Universitas Riau, Pekan Baru, Indonesia

DOI:

https://doi.org/10.37934/araset.50.1.250263

Keywords:

Feature extraction method, feature selection method, breast cancer detection

Abstract

Microwave technology is very promising tool for breast cancer detection. Microwave transmits and receives UWB signals. UWB signals carries information of the breast cancer. UWB signals need to be pre-processed in order to remove irrelevant and redundant features. Feature extraction and feature selection methods are mostly used to remove the unwanted features. In this paper, a statistically modelled feature selection (SMFS) method is proposed for microwave breast cancer detection. Initially, performance of different feature extraction and feature selection method are analysed using Anova test (p-value) and machine learning (SVM, DT, PNN, NB) accuracy. The best feature extraction and feature selection methods are combined and tested. Based on the performance of feature extraction and feature selection method, Combined Neighbour Component Analysis (feature selection) and Statistical features (feature extraction) are combined and tested. This method is able to achieve up to 85%. The result proves two stage methods are able to improve the accuracy compared to single stage method. Therefore, SMFS is able to detect breast cancer efficiently.

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Author Biographies

V. Vijayasarveswari, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

vijaya@unimap.edu.my

Norfadila Mahrom, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

norfadila@unimap.edu.my

Rafikha Aliana A. Raof, Sports Engineering Research Centre (SERC), Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

rafikha@unimap.edu.my

Len Al Eh Kan Phak , Advanced Computing, Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

phaklen@unimap.edu.my

Muhammad Amiruddin Ab Razak, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

s181372362@studentmail.unimap.edu.my

Bavanraj Punniya Silan, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

s181021036@studentmail.unimap.edu.my

Ahmad Ashraf Abdul Halim, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

ashrafhalim@unimap.edu.my

Mohd Wafi Nasrudin, Advanced Computing, Centre of Excellence, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

wafi@unimap.edu.my

Nuraminah Ramli, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Kangar 01000, Perlis, Malaysia

nuraminah@unimap.edu.my

Yusnita Rahayu, Department of Electrical Engineering, Universitas Riau, Pekan Baru, Indonesia

yusnita.rahayu@lecturer.unri.ac.id

Published

2024-08-13

How to Cite

Vijayasarveswari, V., Mahrom, N., A. Raof, R. A., Phak , L. A. E. K., Ab Razak, M. A., Silan, B. P., Abdul Halim, A. A., Nasrudin, M. W., Ramli, N., & Rahayu, Y. (2024). Development of Statistically Modelled Feature Selection Method for Microwave Breast Cancer Detection. Journal of Advanced Research in Applied Sciences and Engineering Technology, 50(1), 250–263. https://doi.org/10.37934/araset.50.1.250263

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