Early Detection of Mild Cognitive Impairment using Neuropsychological Data and Machine Learning Techniques
| Autor: | Samah Alsegehy, Xiong Jiang, Lin-Ching Chang, Ibrahim Almubark |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
Receiver operating characteristic business.industry Neuropsychology Feature selection Neuropsychological test Machine learning computer.software_genre medicine.disease Functional Activities Questionnaire Support vector machine Neuroimaging mental disorders medicine Dementia Artificial intelligence business computer |
| Zdroj: | 2020 IEEE Conference on Big Data and Analytics (ICBDA). |
| Popis: | Individuals with mild cognitive impairment (MCI) have a much higher risk of developing Alzheimer’s disease (AD) or other types of dementia. Early detection of MCI is critical to identify these high-risk candidates for proper management and early interventional treatment. In this paper, a neuropsychological dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), one of the largest public AD database, was used. Four different machine learning algorithms were investigated to classify MCI from demographically matched cognitively normal (CN) older adults. In total, there were 620 subjects, including 391 MCI and 229 CN. A comprehensive neuropsychological battery was administered to all subjects, and 3 tests were selected for this study: Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog), Mini-Mental State Examination (MMSE), and Functional Activities Questionnaire (FAQ). Of all the classifiers tested, the Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel had the best performance for MCI detection, with an average classification accuracy of 88.06% and the area under the receiver operating characteristic curve (AUC) of 0.945. |
| Databáze: | OpenAIRE |
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