Effective biomarkers play important roles for accurate diagnosis of Alzheimer’s Disease (AD), including its intermediate stage (i.e. mild cognitive impairment, MCI). In this paper, a new feature selection scheme was proposed to improve the identification AD and MCI from healthy controls (HC) by a support vector machine (SVM) based-classifier with recursive feature addition. Our method can find the significant features automatically, and the experiments in this work demonstrates that our scheme can achieve better classification performance based on a dataset with 103 subjects where three biomarkers, i.e., structural MR imaging (MRI), functional imaging PET, and cerebrospinal fluid(CSF), had been used. Our proposed method demonstrated its effectiveness in identifying AD from HC with an accuracy of 95.0%, while only 89.3% for the classifier without the step of feature selection. In addition, some features selected in this work had shown strong relation with AD by other previous studies, which can provide the support for the significance of our results.
|Original language||English (US)|
|Title of host publication||Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings|
|Editors||Francisco Ortuno, Ignacio Rojas|
|Number of pages||11|
|State||Published - 2016|
|Event||4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 - Granada, Spain|
Duration: Apr 20 2016 → Apr 22 2016
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016|
|Period||4/20/16 → 4/22/16|
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China (Nos. 61300058, 61472282 and 61374181), Anhui Provincial Natural Science Foundation (No. 1508085MF129). The authors give special thanks to Professor D.Q. Zhang in Nanjing University of Aeronautics and Astronautics for his work in data preprocessing, and the data support from ADNI.
© Springer International Publishing Switzerland 2016.
- Alzheimer’s disease (AD)
- Feature selection (FS)
- Mild cognitive impairment (MCI)
- Support vector machine (SVM)