title: Improve the diagnosis of atrial hypertrophy with the local discriminative support vector machine.
abstract: Computer-aided diagnosis (CAD) approaches succeed in detecting a number of diseases, however, they are not good at addressing atrial hypertrophy disease due to the lack of training data. Support Vector Machine (SVM) is very popular in few CAD solutions to atrial hypertrophy. Yet the performance of SVM is moderate in atrial hypertrophy detection compared to its success in other classification problems. In this paper we propose a novel CAD algorithm, Local Discriminative SVM (LDSVM), to overcome the above-mentioned difficulty. LDSVM consists of a global SVM that is trained on the training data, and a local kNN that is trained based on the information of SVM and query. When a query arrives, SVM gives the initial decision. If the initial decision is less confident, local kNN begins to modify the initial decision. LDSVM improves the accuracy of SVM through some contributions: the risk tube, the discriminant information derived from SVM hyperplane, the new metric and the self-tuning size of neighborhood. Empirical evidence on real medical datasets show high performance of LDSVM over the peers in addressing atrial hypertrophy problems.
authors.affiliation: College of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Ling
authors.affiliation: Department of Training, Air Force Logistics of P. L. A, Xuzhou 221000, China.
authors.forename: Dajin
authors.initials: D
authors.lastname: Gao
authors.affiliation: Department of Training, Air Force Logistics of P. L. A, Xuzhou 221000, China.
authors.forename: Xifeng
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Department of Training, Air Force Logistics of P. L. A, Xuzhou 221000, China.
authors.forename: Zhining
authors.initials: Z
authors.lastname: Huang
authors.affiliation: Department of Training, Air Force Logistics of P. L. A, Xuzhou 221000, China.
authors.forename: Xiangsheng
authors.initials: X
authors.lastname: Rong
mesh_terms: D000465:Algorithms; D006332:Cardiomegaly / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016002:Discriminant Analysis; D004562:Electrocardiography / Q000379:methods*; D006325:Heart Atria / Q000473:pathology; D006801:Humans; D006984:Hypertrophy / Q000175:diagnosis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: Computer-aided diagnosis; derivative of hyperplane function; discriminative direction; support vector machine