Machine Learning Approach to Classify Breast Tissues: A Case Study Using Six-classed Breast Tissue Data

Abstract

The present study investigates the effectiveness of six Machine Learning (ML) algorithms in classifying the breast tissue dataset generated using the electrical impedance spectroscopy method. This study used the breast tissue dataset available at the UCI machine learning repository, consisting of 106 spectral records with ten variables. The data were partitioned into train and test datasets. Sixty six percentage of data was allocated for the train dataset and balance for the test dataset. Six ML algorithms were tested for effectiveness using accuracy, Cohen’s Kappa, sensitivity and specificity. The results revealed that the backpropagation algorithm (BPN) produced the highest accuracy and Kappa compared to other machine learning algorithms in classifying the six-classed breast tissue dataset. Both Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) produced the second-highest accuracy and Kappa. The C5.0 decision tree algorithm takes the third level. The fourth and fifth levels of accuracy are Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ), respectively. The sensitivity of all classes by the classification of BPN was more than eighty percentage, which is higher than other machine learning algorithms. The specificity of all classes predicted by BPN was more than ninety six percentage and was comparatively at the highest level than other machine learning algorithms. Therefore, the study concludes that the backpropagation algorithm will effectively classify the six classed breast tissue data.