Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes

Timely intervention in individuals at risk of dementia is often emphasized, and Mild Cognitive Impairment (MCI) is considered to be an effective precursor to Alzheimers disease (AD), which can be used as an intervention criterion. This paper attempts to use deep learning techniques to recognise MCI in the elderly. Deep learning has recently come to attention with its superior expressive power and performance over conventional machine learning algorithms. The current study uses variations of auto-encoders trained on neuropsychological test scores to discriminate between cognitively normal individuals and those with MCI in a cohort of community dwelling individuals aged 70-90 years. The performance of the auto-encoder classifier is further optimized by creating an ensemble of such classifiers, thereby improving the generalizability as well. In addition to comparable results to those of conventional machine learning algorithms, the auto-encoder based classifiers also eliminate the need for separate feature extraction and selection while also allowing seamless integration of features from multiple modalities.