Classification of ADHD with fMRI data and multi-objective optimization. (November 2020)

• Record Type: Journal Article
• Authors: Shao, Lizhen; You, Yang; Du, Haipeng; Fu, Dongmei
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: fMRI -- Multi-objective optimization -- ADHD -- SVM; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105676
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel multi-objective optimization classification scheme is proposed. The scheme uses a cost sensitive three objective model to handle the class imbalance problem. A preferred subset of pareto optimal classifiers can be obtained based on the decision maker's preference. Results show that the proposed scheme performs considerably better than some traditional methods. Abstract: Background and objective: Dataset imbalance is an important problem in neuroimaging. Imbalanced datasets would cause the performance degradation of a classifier by utilizing imbalanced learning, which tends to overfocus on the majority class. In this paper, we consider an imbalanced neuroimaging classification problem, namely, classification of attention deficit hyperactivity disorder (ADHD) using resting-state functional magnetic resonance imaging. Methods: We propose a multi-objective classification scheme based on support vector machine (SVM). Our scheme addresses the imbalanced dataset problem by using a three objective SVM model with the positive and negative empirical errors being handled explicitly and separately. Moreover, an interactive multi-objective method incorporating the decision maker's preference is adopted, thus a preferred subset of pareto optimal classifiers for decision making can be obtained. Results: The proposed scheme is assessed on five datasets from the ADHD- 200 consortium. Numerical results show that the proposed multi-objective scheme considerably outperforms some traditional classification methods in the literature. Conclusion: The proposed multi-objective classification scheme avoids hyper-parameter selection, it effectively addresses dataset imbalanced problem from algorithm level. The scheme can not only be used in the diagnosis of ADHD but also in the diagnosis of other diseases, such as Alzheimer and Autism etc. Graphical abstract: Image, graphical abstract