Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction

Abstract

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.

Performance Enhancement of Predictive Analytics for Health Informatics Using Dimensionality Reduction Techniques and Fusion Frameworks

“…Similar issues have been found with other disease prediction and management systems developed to work with one modality at a time [2], [24]. Some studies proposed fusion frameworks such as for readmission prediction [25], disease prediction which are specific for the fusion of multi-modal or multi-source data at a time [18], [26]. These systems offer better results than unimodal based system.…”

“…Majumder and Pratihar proposed a multi-sensor fusion via fuzzy clustering for the prediction of heart diseases [21]. Vijayasarveswari et al introduced a multi-phase feature selection approach for cancer prediction [26]. Zhang…”

“…Several techniques have been proposed to combine data from multiple sources and modalities in recent years, which may hold similar and different data formats used for predictive analytics [18], [21], [26]- [28]. These techniques include data integration, data fusion, feature fusion, knowledge fusion, and multi-sensor fusion [4], [21], [26].There is need to develop a system that can fuse multi-source, multi-nature, and multi-modal data to improve the performance of disease diagnostic and prediction systems [5]. Multi-source fusion increases the reliability and availability of data.…”

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

“…Recently, this technology was used to detect the presence, benign and malignant of breast tumors. 30,[34][35][36][37] Song et al compared the time-frequency feature extraction methods of microwave data, and then the presence of tumor was detected based on support vector machine. 38 In Song et al, 39 data points of the received signals were fed into the neural network and the tumor was located.…”

“…On the other hand, artificial intelligence technology with strong learning and representation ability has great attraction and potential in microwave breast cancer detection. Recently, this technology was used to detect the presence, benign and malignant of breast tumors 30,34–37 . Song et al.…”