A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease

Chen Fang, Chunfei Li, Mercedes Cabrerizo, Armando Barreto, Jean Andrian, David Loewenstein, Ranjan Duara, Malek Adjouadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains unsatisfactory, especially for delineating the MCI group from the cognitively normal control (CN) group. This study introduces a novel algorithm based on the Gaussian discriminant analysis (GDA) for a more effective and accurate classification performance. Subjects considered in this study included 190 CN, 305 MCI, and 133 AD subjects. Using 75% of the data as the training set with a tenfold cross validation, the proposed algorithm achieved an average accuracy of 94.17%, a sensitivity of 93.00%, and a specificity of 95.00% for discriminating AD from CN; and an average accuracy of 84.86%, a sensitivity of 84.78%, and a specificity of 85.00% for discriminating MCI from CN. Then a true test was implemented for the remaining 25% data, for discriminating specifically MCI from CN, resulting in an accuracy of 82.20%, a sensitivity of 83.10%, and a specificity of 80.85%. As revealed through the literature, these results involving the delineation of the MCI group from CN could be considered as the best classification performance obtained so far. This study also shows that by separating left and right hemispheres of the brain into two decision spaces, then combining the results of these two spaces, the classification performance can be improved significantly; an assertion proven in this study.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages538-542
Number of pages5
Volume2017-January
ISBN (Electronic)9781509030491
DOIs
StatePublished - Dec 15 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States
Duration: Nov 13 2017Nov 16 2017

Other

Other2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
CountryUnited States
CityKansas City
Period11/13/1711/16/17

Fingerprint

Discriminant Analysis
Discriminant analysis
Learning algorithms
Early Diagnosis
Alzheimer Disease
Learning
Support vector machines
Prodromal Symptoms
Control Groups
Cognitive Dysfunction
Brain

Keywords

  • Alzheimer's disease
  • Gaussian discriminant analysis
  • machine learning
  • mild cognitive impairment

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

Cite this

Fang, C., Li, C., Cabrerizo, M., Barreto, A., Andrian, J., Loewenstein, D., ... Adjouadi, M. (2017). A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease. In Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (Vol. 2017-January, pp. 538-542). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217705

A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease. / Fang, Chen; Li, Chunfei; Cabrerizo, Mercedes; Barreto, Armando; Andrian, Jean; Loewenstein, David; Duara, Ranjan; Adjouadi, Malek.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 538-542.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fang, C, Li, C, Cabrerizo, M, Barreto, A, Andrian, J, Loewenstein, D, Duara, R & Adjouadi, M 2017, A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease. in Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 538-542, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217705
Fang C, Li C, Cabrerizo M, Barreto A, Andrian J, Loewenstein D et al. A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease. In Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 538-542 https://doi.org/10.1109/BIBM.2017.8217705
Fang, Chen ; Li, Chunfei ; Cabrerizo, Mercedes ; Barreto, Armando ; Andrian, Jean ; Loewenstein, David ; Duara, Ranjan ; Adjouadi, Malek. / A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 538-542
@inproceedings{e76985be730e4b908e3c62a86467e222,
title = "A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease",
abstract = "Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains unsatisfactory, especially for delineating the MCI group from the cognitively normal control (CN) group. This study introduces a novel algorithm based on the Gaussian discriminant analysis (GDA) for a more effective and accurate classification performance. Subjects considered in this study included 190 CN, 305 MCI, and 133 AD subjects. Using 75{\%} of the data as the training set with a tenfold cross validation, the proposed algorithm achieved an average accuracy of 94.17{\%}, a sensitivity of 93.00{\%}, and a specificity of 95.00{\%} for discriminating AD from CN; and an average accuracy of 84.86{\%}, a sensitivity of 84.78{\%}, and a specificity of 85.00{\%} for discriminating MCI from CN. Then a true test was implemented for the remaining 25{\%} data, for discriminating specifically MCI from CN, resulting in an accuracy of 82.20{\%}, a sensitivity of 83.10{\%}, and a specificity of 80.85{\%}. As revealed through the literature, these results involving the delineation of the MCI group from CN could be considered as the best classification performance obtained so far. This study also shows that by separating left and right hemispheres of the brain into two decision spaces, then combining the results of these two spaces, the classification performance can be improved significantly; an assertion proven in this study.",
keywords = "Alzheimer's disease, Gaussian discriminant analysis, machine learning, mild cognitive impairment",
author = "Chen Fang and Chunfei Li and Mercedes Cabrerizo and Armando Barreto and Jean Andrian and David Loewenstein and Ranjan Duara and Malek Adjouadi",
year = "2017",
month = "12",
day = "15",
doi = "10.1109/BIBM.2017.8217705",
language = "English (US)",
volume = "2017-January",
pages = "538--542",
booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - A Gaussian discriminant analysis-based generative learning algorithm for the early diagnosis of mild cognitive impairment in Alzheimer's disease

AU - Fang, Chen

AU - Li, Chunfei

AU - Cabrerizo, Mercedes

AU - Barreto, Armando

AU - Andrian, Jean

AU - Loewenstein, David

AU - Duara, Ranjan

AU - Adjouadi, Malek

PY - 2017/12/15

Y1 - 2017/12/15

N2 - Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains unsatisfactory, especially for delineating the MCI group from the cognitively normal control (CN) group. This study introduces a novel algorithm based on the Gaussian discriminant analysis (GDA) for a more effective and accurate classification performance. Subjects considered in this study included 190 CN, 305 MCI, and 133 AD subjects. Using 75% of the data as the training set with a tenfold cross validation, the proposed algorithm achieved an average accuracy of 94.17%, a sensitivity of 93.00%, and a specificity of 95.00% for discriminating AD from CN; and an average accuracy of 84.86%, a sensitivity of 84.78%, and a specificity of 85.00% for discriminating MCI from CN. Then a true test was implemented for the remaining 25% data, for discriminating specifically MCI from CN, resulting in an accuracy of 82.20%, a sensitivity of 83.10%, and a specificity of 80.85%. As revealed through the literature, these results involving the delineation of the MCI group from CN could be considered as the best classification performance obtained so far. This study also shows that by separating left and right hemispheres of the brain into two decision spaces, then combining the results of these two spaces, the classification performance can be improved significantly; an assertion proven in this study.

AB - Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer's disease (AD) and its prodromal stage of mild cognitive impairment (MCI). For solving this high dimensional classification problem, the widely used algorithm remains to be Support Vector Machines (SVM). But due to the high variance of the data, the classification performance of SVM remains unsatisfactory, especially for delineating the MCI group from the cognitively normal control (CN) group. This study introduces a novel algorithm based on the Gaussian discriminant analysis (GDA) for a more effective and accurate classification performance. Subjects considered in this study included 190 CN, 305 MCI, and 133 AD subjects. Using 75% of the data as the training set with a tenfold cross validation, the proposed algorithm achieved an average accuracy of 94.17%, a sensitivity of 93.00%, and a specificity of 95.00% for discriminating AD from CN; and an average accuracy of 84.86%, a sensitivity of 84.78%, and a specificity of 85.00% for discriminating MCI from CN. Then a true test was implemented for the remaining 25% data, for discriminating specifically MCI from CN, resulting in an accuracy of 82.20%, a sensitivity of 83.10%, and a specificity of 80.85%. As revealed through the literature, these results involving the delineation of the MCI group from CN could be considered as the best classification performance obtained so far. This study also shows that by separating left and right hemispheres of the brain into two decision spaces, then combining the results of these two spaces, the classification performance can be improved significantly; an assertion proven in this study.

KW - Alzheimer's disease

KW - Gaussian discriminant analysis

KW - machine learning

KW - mild cognitive impairment

UR - http://www.scopus.com/inward/record.url?scp=85045960873&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85045960873&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2017.8217705

DO - 10.1109/BIBM.2017.8217705

M3 - Conference contribution

VL - 2017-January

SP - 538

EP - 542

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -