| Summary: | Background: Machine learning and data mining techniques have been successfully applied on MRI images for detecting Alzheimer's disease (AD). But only a few studies have explored the possibility of AD detection from non-image data. These studies have applied traditional data visualization and classification algorithms. There is a need for new sophisticated learning algorithms, for detecting and quantifying the severity of AD by exploring the complex interactions between the features in AD subjects.Method: In this work, a supervised learning model to effectively capture the complex feature interactions, in the sample space of AD data, is presented for knowledge discovery. The discovered knowledge is further used to quantify the similarity of a test subject to the demented class.Results: Evaluation of the proposed model, on OASIS database of Alzheimer's subjects, validates the well established risk factors and identifiers for AD: Age, Socio-Economic Status, MMSE Score and Whole Brain Volume. The Test subjects are affiliated to either non-demented (ND) or AD class, with non-overlapping and measurable similarity indices: Female ND (CDR=0) [0.48â2.90], Female AD (CDR=0.5) [90.16â774.51], Female AD (CDR=1) [1633.90â7182.23], Female AD (CDR=2) [55258.51â66382.44], Male ND (CDR=0)[0.69â3.66], Male AD (CDR=0.5) [99.18â647.51] and Male AD (CDR=1) [3880.16â6519.40].Conclusion: The outcome of the work clearly demonstrates that, supervised learning model can be used effectively to quantify the severity of AD on a standard measurable scale. This scale of distance can be used as a supplement for clinical dementia rating. Keywords: Alzheimer's disease, Dementia, Multifactor dimensionality reduction, Knowledge discovery, Similarity measure, Affiliation analysis
|
|---|
