Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data - Ogunbiyi, Temitope; Adegoke, Michael; Oluwatosin, Adebisi; Aremo, Bamidele; Adekunle, Olufemi; Ayoariyo, Emmanuel; Udemba, Austin (International Journal of Data Science, 2024)
This study presents an approach to improving the classification of acute leukemia subtypes using gene expression data analysis. Leveraging Recursive Feature Elimination (RFE) as a feature selection technique and Multilayer Perceptron (MLP) as the predictive modeling framework, this research aims to identify the most influential genes for distinguishing between Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) cases. RFE systematically ranks and selects the most discriminative gene attributes, while MLP constructs a predictive model based on these attributes. The results demonstrate the effectiveness of this combined approach, achieving precision, accuracy, F1-Score, and recall rates of approximately 99% for leukemia subtype classification. Furthermore, specific genes contributing most to the model’s predictive power and shedding light on potential biomarkers for leukemia diagnosis were identified. This research underscores the significance of RFE and MLP in the analysis of gene expression data and their potential impact on clinical decision-making in the field of oncology.