Mild Cognitive Impairment Diagnosis Using Neuropsychological Tests and Agile Machine Learning

Alzheimer’s Disease constitutes one of the biggest portions of the diseases related to ageing. Mild Cognitive Impairment may be considered the formative stage of this disease. The automated diagnosis of Mild Cognitive Impairment using Machine Learning will help the clinicians in delaying its progression and will be easy, cheap, and efficient for the patient. This work uses neuropsychological data obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI), containing the results from 12 tests including Mini-Mental State Examination and ADAS-Cog. An extensive empirical analysis is carried out and the most important features are extracted using the proposed pipelines. The Feature Selection is done using both filter and wrapper methods and in total 13 features were selected. It was found that most of the selected features related to tasks associated with memory. The proposed method gives a performance of 0.9817 in terms of F1 Score. Thus, performing better vis-à-vis the state of the art. The proposed pipeline helps to reduce the number of neuropsychological tests to diagnose the disease. This work is one of the components of the projects that use multi-modality data including structural-Magnetic Resonance Imaging, functional-Magnetic Resonance Imaging, Positron Emission Tomography and Neuropsychological data to develop a system for efficient and effective diagnosis of MCI. The project management is done using Agile Methodology. The results are encouraging and pave the way for the development of such a system.