UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set

Authors

  • Ahmad Ashraf Abdul Halim
  • Vijayasarveswari Veeraperumal Faculty of Electronic Engineering Technology (FTKEN), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia
  • Allan Melvin Andrew Faculty of Electrical Engineering Technology (FTKE), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia
  • Mohd Najib Mohd Yasin Faculty of Electronic Engineering Technology (FTKEN), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia
  • Mohd Zamri Zahir Ahmad Faculty of Electronic Engineering Technology (FTKEN), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia
  • Kabir Hossain Norwegian University of Science and Technology (NTNU), N-2815 Gjøvik, Norway
  • Bifta Sama Bari Faculty of Electrical and Electronic Engineering Technology (FTKEE), Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia
  • Fatinnabila Kamal Faculty of Applied and Human Sciences (FSGM), Universiti Malaysia Perlis, 01000, Kangar, Perlis Malaysia

DOI:

https://doi.org/10.37934/araset.29.2.8190

Keywords:

Breast cancer detection, Feature selection, Machine learning

Abstract

Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use.

Author Biographies

Vijayasarveswari Veeraperumal, Faculty of Electronic Engineering Technology (FTKEN), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

vijaya@unimap.edu.my

Allan Melvin Andrew, Faculty of Electrical Engineering Technology (FTKE), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

allanmelvin@unimap.edu.my

Mohd Najib Mohd Yasin, Faculty of Electronic Engineering Technology (FTKEN), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

najibyasin@unimap.edu.my

Mohd Zamri Zahir Ahmad, Faculty of Electronic Engineering Technology (FTKEN), Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia

zamrizahir@unimap.edu.my

Kabir Hossain, Norwegian University of Science and Technology (NTNU), N-2815 Gjøvik, Norway

hossain.kabir42@gmail.com

Bifta Sama Bari, Faculty of Electrical and Electronic Engineering Technology (FTKEE), Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

biftasama.eee@gmail.com

Fatinnabila Kamal, Faculty of Applied and Human Sciences (FSGM), Universiti Malaysia Perlis, 01000, Kangar, Perlis Malaysia

fatinnabila@unimap.edu.my

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Published

2023-01-06

How to Cite

Ahmad Ashraf Abdul Halim, Vijayasarveswari Veeraperumal, Allan Melvin Andrew, Mohd Najib Mohd Yasin, Mohd Zamri Zahir Ahmad, Kabir Hossain, Bifta Sama Bari, & Fatinnabila Kamal. (2023). UWB-Based Early Breast Cancer Existence Prediction Using Artificial Intelligence for Large Data Set. Journal of Advanced Research in Applied Sciences and Engineering Technology, 29(2), 81–90. https://doi.org/10.37934/araset.29.2.8190

Issue

Vol. 29 No. 2: January (2023)

Section

Articles

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