Enhanced Leukemia Subtype Classification Using SMOTE and Hybrid Feature Selection in Microarray Data - P C, Chaitra; Kumar, R Saravana (Informatica, 2026)
Blood cancer is a rising issue in the past decade, and early detection is a must for early intervention. Traditional techniques for diagnosing blood cancer include high expense, long processes, and medical professionals and a variety of tests. Hence, an effective prediction model with high accuracy is a must. This study presents a robust leukemia multiclass classification framework leveraging advanced ML (machine learning) techniques. Addressing key challenges such as class imbalance, high-dimensional gene expression data, and feature selection. This study presents an integrated approach for data balancing by combining the Synthetic Minority Oversampling Technique (SMOTE) with nonlinear interpolation. A hybrid feature selection model utilizing Principal Component Analysis (PCA) on Linear Discriminant Analysis (LDA) is implemented to enhance classification performance. Experimental results indicate that using SMOTE with PCA+LDA on Random Forest classifiers outperforms traditional methods, achieving 98% accuracy in leukemia multiclass classification.