Citation: Halim, A.A.A.; Andrew, A.M.; Mustafa, W.A.; Mohd Yasin, M.N.; Jusoh, M.; Veeraperumal, V.; Abd Rahman, M.A.; Zamin, N.; Mary, M.R.; Khatun, S. Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver. Diagnostics 2022, 12, 2870. https://doi.org/10.3390/ diagnostics12112870 Academic Editor: Andreas Kjaer Received: 17 October 2022 Accepted: 16 November 2022 Published: 19 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article Optimized Intelligent Classifier for Early Breast Cancer Detection Using Ultra-Wide Band Transceiver Ahmad Ashraf Abdul Halim 1,2 , Allan Melvin Andrew 1,2, * , Wan Azani Mustafa 3,4, * , Mohd Najib Mohd Yasin 1,2, * , Muzammil Jusoh 1,2 , Vijayasarveswari Veeraperumal 1,2 , Mohd Amiruddin Abd Rahman 5 , Norshuhani Zamin 6 , Mervin Retnadhas Mary 6 and Sabira Khatun 7 1 Advanced Communication Engineering (ACE), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), No. 15 & 17, Jalan Tiga, Pengkalan Jaya Business Centre, Kangar 01000, Perlis, Malaysia 2 Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau 02600, Perlis, Malaysia 3 Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau 02600, Perlis, Malaysia 4 Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau 02600, Perlis, Malaysia 5 Department of Physics, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia 6 College of Computing, Saudi Electronic University (SEU), Riyadh 13316, Saudi Arabia 7 Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang (UMP), Pekan 26600, Pahang, Malaysia * Correspondence: allanmelvin@unimap.edu.my (A.M.A.); wanazani@unimap.edu.my (W.A.M.); najibyasin@unimap.edu.my (M.N.M.Y.) Abstract: Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample. Keywords: feature selection; prediction; feature engineering; multi-stage; machine learning; supervised learning; breast cancer 1. Introduction Breast cancer is the most common cancer worldwide and the leading cancer compared to other types of cancer for women [1]. It is the fifth most-frequent cancer that causes death in women, especially in developing countries, where screening systems are limited and sometimes nearly non-existent [2,3]. Previous studies have stated that early breast Diagnostics 2022, 12, 2870. https://doi.org/10.3390/diagnostics12112870 https://www.mdpi.com/journal/diagnostics