A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification

Abstract

It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN–SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN–SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

“…By incorporating these innovative approaches, researchers have significantly enhanced the performance of machine learning and deep learning models in classification of lung cancer, ultimately contributing to more accurate diagnoses and better patient outcomes. This integration of cutting-edge techniques has brought about a paradigm shift in the field of lung cancer detection [13][14][15][16][17][18].…”

Energy Management of Microgrids with a Smart Charging Strategy for Electric Vehicles Using an Improved RUN Optimizer

“…The RUN optimizer is a novel optimizer that mimics solving the differential equations. The optimizer is used for solving several problems like optimal ratings of microgrid components [16], hydropower plant operation [17], PV parameters [18], photovoltaic modeling [19], energy management with demand-side response [20], fractional-order design of batteries [21], optimal PID of an AVR system [22], cancer classification [23] and the equations of energy balance for an inverted absorber [24].…”