Application of fuzzy logic for Alzheimer's disease diagnosis

被引：0
|
作者：
Krashenyi, Igor [1 ]
Popov, Anton [1 ]
Ramirez, Javier [2 ]
Manuel Gorriz, Juan [2 ]
机构：
[1] Natl Tech Univ Ukraine, Kyiv Polytech Inst, Phys & Biomed Elect Dept, Kiev, Ukraine
[2] Univ Granada, Dept Signal Theory Telemat & Commun, Granada, Spain
来源：
2015 Signal Processing Symposium (SPSympo) | 2015年
关键词：
Alzheimer's disease; MRI; classification; mild-cognitive impairment; fuzzy logic; CLASSIFICATION; MORPHOMETRY; MRI;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Fuzzy Inference System (FIS) is developed using subtractive clustering algorithm, and applied to classification between MRI images of patients having Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) and Normal Controls (NC). Features used as FIS inputs are mean values and standard deviations in intensities from most descriptive brain regions. k-fold cross-validation was used to estimate FIS performance, resulting in accuracy, sensitivity, specificity and positive predictive value (ppv) characteristics of FIS classification between different groups. ppv was equal to 0.8778 +/- 0.0088 (AD vs. NC), 0.7289 +/- 0.0243 (NC vs. MCI), and 0.8531 +/- 0.0069 (MCI vs. AD).
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页数：4
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