Hybrid Machine Learning Model of Extreme Learning Machine Radial basis function for Breast Cancer Detection and Diagnosis; a Multilayer Fuzzy Expert System

TL;DR

This paper introduces a multilayer fuzzy expert system utilizing an Extreme Learning Machine with RBF kernel for breast cancer detection, demonstrating superior performance over linear SVM on the Wisconsin dataset.

Contribution

It presents a novel hybrid model combining ELM-RBF with a fuzzy expert system for improved breast cancer diagnosis accuracy.

Findings

ELM-RBF outperforms linear SVM in RMSE, R2, MAPE metrics.

ELM-RBF shows better accuracy, precision, sensitivity, specificity.

The model achieves higher true positive rate and lower false-negative rate.

Abstract

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R2, MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.