Wrapper-Based Feature Selection for Medical Diagnosis: The BTLBO-KNN Algorithm
Fateh Seghir, Ahlem Drif, Saadeddine Selmani, Hocine Cherifi
Abstract
Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved Binary Teaching-Learning Based Optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner’s replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.