Design of an Enhanced Fuzzy k-nearest Neighbor Classifier Based Computer Aided Diagnostic System for Thyroid Disease

• Published in: Journal of medical systems Vol. 36; no. 5; pp. 3243 - 3254
• Main Authors: Liu, Da-You, Chen, Hui-Ling, Yang, Bo, Lv, Xin-En, Li, Li-Na, Liu, Jie
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01.10.2012; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial intelligence; Classification; Classifiers; Computer aided design; Diagnosis, Computer-Assisted - methods; Diagnostic systems; Fuzzy; Fuzzy Logic; Fuzzy set theory; Health Informatics; Health Sciences; Medical diagnosis; Medicine; Medicine & Public Health; Original Paper; Principal Component Analysis; Statistics for Life Sciences; Systems design; Thyroid diseases; Thyroid Diseases - diagnosis; Thyroid disease diagnosis; Fuzzy; nearest neighbor (FKNN); Particle swarm optimization (PSO); Computer aided diagnostic (CAD) system
• ISSN: 0148-5598; 1573-689X
• DOI: 10.1007/s10916-011-9815-x

In this paper, we present an enhanced fuzzy k -nearest neighbor (FKNN) classifier based computer aided diagnostic (CAD) system for thyroid disease. The neighborhood size k and the fuzzy strength parameter m in FKNN classifier are adaptively specified by the particle swarm optimization (PSO) approach. The adaptive control parameters including time-varying acceleration coefficients (TVAC) and time-varying inertia weight (TVIW) are employed to efficiently control the local and global search ability of PSO algorithm. In addition, we have validated the effectiveness of the principle component analysis (PCA) in constructing a more discriminative subspace for classification. The effectiveness of the resultant CAD system, termed as PCA-PSO-FKNN, has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far via 10-fold cross-validation (CV) analysis, with the mean accuracy of 98.82% and with the maximum accuracy of 99.09%. Promisingly, the proposed CAD system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.