Expansion of Regularized Kmeans Discretization Machine Learning Approach in Prognosis of Dementia Progression
Afreen Khan, Swaleha Zubair · 2020
Dementia, a fatal and progressive neurodegenerative disease, poses a huge challenge to health care fraternity. A timely diagnosis of this intricate disorder can help in designing and formulating strategies helpful in effective regulation of the disease. There exists a huge amount of multi-variate heterogeneous clinical data which can be developed by examining MRI of the brain of the inflicted patients. Discretization is a widely employed method which transforms continuous attributes into discrete attributes by building a group of contiguous intervals. This helps in extending the range of the attribute's values. It is employed to handle the outlier problem that may have a noteworthy effect on the generalization performance of the model. In this study, we propose a machine learning-based probabilistic approach aimed to evaluate the dementia progression in cross-sectional Magnetic Resonance Imaging (MRI) data of demented and nondemented adults. We analyzed a dataset based on 416 subjects, aged between 18 to 96 years. Initially, we built a ML model employing discretization and non-discretization on the MRI dataset. Next, we employed kmeans strategy and three encoding techniques viz. ordinal, onehot-dense and onehot. Clinical Dementia Rating (CDR) score aided in determining the classes of dementia. Besides overcoming inherent adverse outlier issues, the proposed approach helped in spreading the values of a skewed attribute throughout a set of bins along with the same number of observations. We compared the performance of 18 employed ML classifiers. Experimental results on the MRI data showed noteworthy relative improvement in the pattern analysis of both discretization and non-discretization approach.