Compact Graph based Semi-Supervised Learning for Medical Diagnosis in Alzheimer's Disease

• Published in: IEEE signal processing letters Vol. 21; no. 10; pp. 1192 - 1196
• Main Authors: Mingbo Zhao, Chan, Rosa H. M., Chow, Tommy W. S., Peng Tang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Alzheimer's disease; Classification; Classification algorithms; Compact graph construction; Dementia; Diagnosis; graph based semi-supervised learning; Graphs; Learning; Medical; Medical diagnosis; Preserves; Progressions; Semisupervised learning; Sensitivity; Supervised learning; Compact graph construction; medical diagnosis; graph based semi-supervised learning
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2014.2329056

Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering dementia is important for early diagnosis in order to slow down the disease progression and help preserve some cognitive functions of the brain. To achieve accurate classification, significant amount of subject feature information are involved. Hence identification of demented subjects can be transformed into a pattern classification problem. In this letter, we introduce a graph based semi-supervised learning algorithm for Medical Diagnosis by using partly labeled samples and large amount of unlabeled samples. The new method is derived by a compact graph that can well grasp the manifold structure of medical data. Simulation results show that the proposed method can achieve better sensitivities and specificities compared with other state-of-art graph based semi-supervised learning methods.