PRFE-driven gene selection with multi-classifier ensemble for cancer classification

In this era, cancer remains a paramount concern due to its pervasive impact on individuals and societies, persistent challenges in treatment and prevention, and the ongoing need for global collaboration and innovation to improve outcomes and reduce its burden. Cancer marked by uncontrolled cell growth is a leading global cause of mortality, necessitating advanced methods for accurate diagnosis. This study introduces an innovative unsupervised feature selection technique Principal Recursive Feature Eliminator (PRFE) for selection of genes and cancer classification. Subsequently, seven different classifiers—Support Vector Machine, Random Forest, CatBoost, Light Gradient Boosting Method, Artificial Neural Network, Convolutional Neural Network, Long Short-Term Memory are used to increase the model’s robustness. The proposed approach is evaluated on nine benchmark gene expression datasets with a combination of two different algorithms. A series of experiments are conducted to assess the proposed method, focusing on the selected features and identifying optimal classifiers. We have calculated F1-Score, accuracy, recall, and precision. The suggested strategy performs better than expected, as the results highlight its potential to improve cancer classification techniques with an accuracy of 99.98%. We conclude from this analysis that, across many datasets, the CatBoost and CNN model outperforms the other models. This research contributes to the ongoing efforts to improve diagnostic precision and treatment outcomes in cancer research.