A hybrid computational approach for efficient Alzheimer\u2019s disease classification based on heterogeneous data

Xuemei Ding,Dave H Clarke,Liam Maguire,Hui Wang,Kongfatt Wong-Lin,Anthony John Bjourson,Le Roy C Dowey,David H Glass,Maurice O’Kane,Haiying Wang,Magda Bucholc,Girijesh Prasad

doi:10.1038/s41598-018-27997-8

Xuemei Ding, Dave H Clarke + Show 10 more

Open Access

https://doi.org/10.1038/s41598-018-27997-8

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Abstract

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis. As AD is inherently dynamic, it is also not clear how the relationships among AD indicators vary over time. To address these issues, we propose a hybrid computational approach for AD classification and evaluate it on the heterogeneous longitudinal AIBL dataset. Specifically, using clinical dementia rating as an index of AD severity, the most important indicators (mini-mental state examination, logical memory recall, grey matter and cerebrospinal volumes from MRI and active voxels from PiB-PET brain scans, ApoE, and age) can be automatically identified from parallel data mining algorithms. In this work, Bayesian network modelling across different time points is used to identify and visualize time-varying relationships among the significant features, and importantly, in an efficient way using only coarse-grained data. Crucially, our approach suggests key data features and their appropriate combinations that are relevant for AD severity classification with high accuracy. Overall, our study provides insights into AD developments and demonstrates the potential of our approach in supporting efficient AD diagnosis.

Highlights

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis
We considered the following features: (i) the apolipoprotein E (ApoE) allele type instead of genome sequence data; (ii) the total number of active pixels (PET) and the total volume (MRI) from brain imaging data instead of the data associated with the specific brain region; (iii) overall scores from psychological/functional tests instead of specific questions from a test; and (iv) the overall neurological history instead of information on specific neurological disorders
Clinical dementia rating (CDR) is designed to stage the severity of AD based on the state of participants in terms of memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care[24]

Summary

Introduction

There is currently a lack of an efficient, objective and systemic approach towards the classification of Alzheimer’s disease (AD), due to its complex etiology and pathogenesis. A hybrid model, combining a feature reduction technique using rough sets and a genetic algorithm and an uncertain reasoning technique based on Bayesian networks (BN), was proposed in[18], but only psychological/functional assessments were conducted and the obtained BN did not show the strength of the corresponding relationships among the assessments, nor the evolution of the BNs across time It is still not completely known what factors are relatively more important than others with respect to AD, and how they can be influenced under certain conditions or stages of the disease. We apply a combination of complementary data mining and BN modelling approaches on a heterogeneous longitudinal dataset to efficiently identify key features from coarse-grained data and understand probabilistic dependencies among multiple AD factors and their changes over time.

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