Classifying subtypes of acute lymphoblastic leukemia using silhouette statistics and genetic algorithms.

Correct classification and prediction of tumor cells is essential for a successful diagnosis and reliable future treatment. In this study, we aimed at using genetic algorithms for feature selection and proposed silhouette statistics as a discriminant function to distinguish between six subtypes of pediatric acute lymphoblastic leukemia by using microarray with thousands of gene expressions. Our methods have shown a better classification accuracy than previously published methods and obtained a set of genes effective to discriminate subtypes of pediatric acute lymphoblastic leukemia. Furthermore, the use of silhouette statistics, offering the advantages of measuring the classification quality by a graphical display and by an average silhouette width, has also demonstrated feasibility and novelty for more difficult multiclass tumor prediction problems.