research-article

Heterogeneous data fusion for alzheimer's disease study

Authors:

Gene Alexander,

Eric ReimanAuthors Info & Claims

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1025 - 1033

https://doi.org/10.1145/1401890.1402012

Published: 24 August 2008 Publication History

Abstract

Effective diagnosis of Alzheimer's disease (AD) is of primary importance in biomedical research. Recent studies have demonstrated that neuroimaging parameters are sensitive and consistent measures of AD. In addition, genetic and demographic information have also been successfully used for detecting the onset and progression of AD. The research so far has mainly focused on studying one type of data source only. It is expected that the integration of heterogeneous data (neuroimages, demographic, and genetic measures) will improve the prediction accuracy and enhance knowledge discovery from the data, such as the detection of biomarkers. In this paper, we propose to integrate heterogeneous data for AD prediction based on a kernel method. We further extend the kernel framework for selecting features (biomarkers) from heterogeneous data sources. The proposed method is applied to a collection of MRI data from 59 normal healthy controls and 59 AD patients. The MRI data are pre-processed using tensor factorization. In this study, we treat the complementary voxel-based data and region of interest (ROI) data from MRI as two data sources, and attempt to integrate the complementary information by the proposed method. Experimental results show that the integration of multiple data sources leads to a considerable improvement in the prediction accuracy. Results also show that the proposed algorithm identifies biomarkers that play more significant roles than others in AD diagnosis.

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Index Terms

Heterogeneous data fusion for alzheimer's disease study
1. Applied computing
  1. Life and medical sciences
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2008

1116 pages

ISBN:9781605581934

DOI:10.1145/1401890

General Chair:
Ying Li
Microsoft adCenter Labs
,
Program Chairs:
Bing Liu
University of Illinois at Chicago
,
Sunita Sarawagi
Indian Institute of Technology, Bombay

Copyright © 2008 ACM.

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Published: 24 August 2008

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August 24 - 27, 2008

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KDD '08 Paper Acceptance Rate 118 of 593 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1177/25424823241284464
Ahanger AAalam SAssad AMacha MBhat M(2024)Alzhinet: an explainable self-attention based classification model to detect Alzheimer from 3D volumetric MRI dataInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02377-wOnline publication date: 2-Jun-2024
https://doi.org/10.1007/s13198-024-02377-w
Chamma AEngemann DThirion BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Statistically valid variable importance assessment through conditional permutationsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669081(67662-67685)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669081
Garg NChoudhry MBodade R(2023)A review on Alzheimer’s disease classification from normal controls and mild cognitive impairment using structural MR imagesJournal of Neuroscience Methods10.1016/j.jneumeth.2022.109745384(109745)Online publication date: Jan-2023
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