A Machine Learning-based robust approach to identify Dementia progression employing Dimensionality Reduction in Cross-Sectional MRI data

Timely uncovering of various dementia stages is vital in formulating effective treatment strategies for Alzheimer disease. The high-resolution MRI can be progressively exploited in the classification of various stages of dementia that in turn help in the development of efficient therapeutic stratagems. Considering the fact that analysis of the huge volume of data is a tenacious challenge, we explored the competence of machine learning (ML) based algorithms to identify stages of dementia in inflicted patients. The employed dimensionality reduction approach relied on the cross-sectional dataset of 434 MRI sessions of 416 subjects, aged between 18 to 96 years. A five-step strategy involving Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Neighbourhood Component Analysis (NCA), Factor Analysis (FA), and Fast Independent Component Analysis (FastICA) was developed. Next, numerous supervised machine learning algorithms were explored to classify the input data. The as-developed method attained an overall accuracy of 87 percent, which means a noteworthy improvement over the existing classical ML approach.