A novel multi-objective medical feature selection compass method for binary classification

The use of Artificial Intelligence in medical decision support systems has been widely studied. Since a medical decision is frequently the result of a multi-objective optimization problem, a popular challenge combining Artificial Intelligence and Medicine is Multi-Objective Feature Selection (MOFS). This article proposes a novel approach for MOFS applied to medical binary classification. It is built upon a Genetic Algorithm and a 3 -Dimensional Compass that aims at guiding the search towards a desired trade-off between: Number of features, Accuracy and Area Under the ROC Curve (AUC). This method, the Genetic Algorithm with multi-objective Compass (GAwC), outperforms all other competitive genetic algorithm-based MOFS approaches on several real-world medical datasets. Moreover, by considering AUC as one of the objectives, GAwC guarantees the classification quality of the solution it provides thus making it a particularly interesting approach for medical problems where both healthy and ill patients should be accurately detected. Finally, GAwC is applied to a real-world medical classification problem and its results are discussed and justified both from a medical point of view and in terms of classification quality.

Automated summary

This article presents a novel approach for Multi-Objective Feature Selection (MOFS) in medical binary classification using a Genetic Algorithm and a 3-Dimensional Compass. The proposed method, Genetic Algorithm with multi-objective Compass (GAwC), outperforms other genetic algorithm-based MOFS approaches on several real-world medical datasets, guaranteeing classification quality by considering AUC as one of the objectives.