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

Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally, microarrays include several genes and may cause confusion or false prediction. Therefore, the Arithmetic Optimization Algorithm (AOA) is used to identify the optimal gene subset to reduce the number of selected genes. Which can allow the classifiers to yield the best performance for lung cancer classification. In addition, we proposed a modified version of AOA which can work effectively on the high dimensional dataset. In the modified AOA, the features are ranked by their weights and are used to initialize the AOA population. The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies. Finally, the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation. The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10% to 25%. Moreover, the approach significantly enhances lung cancer prediction accuracy. For instance, Lung_Harvard1 achieved an accuracy of 97.5%, Lung_Harvard2 and Lung_Michigan datasets both achieved 100%, Lung_Adenocarcinoma obtained an accuracy of 88.2%, and Lung_Ontario achieved an accuracy of 87.5%. In conclusion, the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.