Open AccessMulti-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction
Author(s) -
V. Vijayasarveswari,
A. M. Andrew,
Muzammil Jusoh,
Thennarasan Sabapathy,
Rafikha Aliana A. Raof,
Mohd Najib Mohd Yasin,
R. Badlishah Ahmad,
Sabira Khatun,
Hasliza A. Rahim
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0229367
Subject(s) - feature selection , computer science , breast cancer , artificial intelligence , normalization (sociology) , naive bayes classifier , pattern recognition (psychology) , support vector machine , feature extraction , linear discriminant analysis , cross validation , artificial neural network , machine learning , data mining , cancer , medicine , sociology , anthropology
Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.