Bayesian classification with local probabilistic model assumption in aiding medical diagnosis

Bin Hu; Chengsheng Mao; Xiaowei Zhang; Yongqiang Dai

doi:10.1109/BIBM.2015.7359770

Bayesian classification with local probabilistic model assumption in aiding medical diagnosis

Bin Hu, Chengsheng Mao, Xiaowei Zhang, Yongqiang Dai

Preventive Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

In computer-aided diagnosis, a Bayesian classifier that can give the class membership probabilities should be more favorable than classifiers that only give a class assertion. In Bayesian classification, an important and critical step is the probability distribution estimation for each class. Existing methods usually estimate the probability distribution in the whole sample space where the original distribution may be too complex to model. In this paper, we propose a probability distribution estimation method based on local probabilistic model assumption. In our method, the estimation of global probability for a certain point is transformed to the computation of local distribution in a small region, where the local distribution is supposed to be simpler and can be assumed as a simpler probabilistic model. By this method, we implement the Bayesian classifiers based on several local probabilistic model assumptions, and experiments with these classifier have been conducted on several real-word biological and medical datasets; the experimental results demonstrate the efficacy of the proposed method for probabilistic classification in medical diagnosis.

Original language	English (US)
Title of host publication	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Editors	lng. Matthieu Schapranow, Jiayu Zhou, Xiaohua Tony Hu, Bin Ma, Sanguthevar Rajasekaran, Satoru Miyano, Illhoi Yoo, Brian Pierce, Amarda Shehu, Vijay K. Gombar, Brian Chen, Vinay Pai, Jun Huan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	691-694
Number of pages	4
ISBN (Electronic)	9781467367981
DOIs	https://doi.org/10.1109/BIBM.2015.7359770
State	Published - Dec 16 2015
Event	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States Duration: Nov 9 2015 → Nov 12 2015

Publication series

Name	Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

Other

Other	IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
Country/Territory	United States
City	Washington
Period	11/9/15 → 11/12/15

Funding

This work was supported by the National Basic Research Program of China (2014CB744600), the National Natural Science Foundation of China (61402211, 61063028 and 61210010) and the International Cooperation Project of Ministry of Science and Technology (20 13DFA 11 140).

Keywords

Bayesian classification
computer-aided diagnosis
local learning
probabilistic model

ASJC Scopus subject areas

Software
Artificial Intelligence
Health Informatics
Biomedical Engineering

Access to Document

10.1109/BIBM.2015.7359770

Cite this

Hu, B., Mao, C., Zhang, X., & Dai, Y. (2015). Bayesian classification with local probabilistic model assumption in aiding medical diagnosis. In L. M. Schapranow, J. Zhou, X. T. Hu, B. Ma, S. Rajasekaran, S. Miyano, I. Yoo, B. Pierce, A. Shehu, V. K. Gombar, B. Chen, V. Pai, & J. Huan (Eds.), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 691-694). Article 7359770 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359770

Hu, Bin ; Mao, Chengsheng ; Zhang, Xiaowei et al. / Bayesian classification with local probabilistic model assumption in aiding medical diagnosis. Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. editor / lng. Matthieu Schapranow ; Jiayu Zhou ; Xiaohua Tony Hu ; Bin Ma ; Sanguthevar Rajasekaran ; Satoru Miyano ; Illhoi Yoo ; Brian Pierce ; Amarda Shehu ; Vijay K. Gombar ; Brian Chen ; Vinay Pai ; Jun Huan. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 691-694 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

@inproceedings{37122be2d4a4471a9ac20de7049cb9d2,

title = "Bayesian classification with local probabilistic model assumption in aiding medical diagnosis",

abstract = "In computer-aided diagnosis, a Bayesian classifier that can give the class membership probabilities should be more favorable than classifiers that only give a class assertion. In Bayesian classification, an important and critical step is the probability distribution estimation for each class. Existing methods usually estimate the probability distribution in the whole sample space where the original distribution may be too complex to model. In this paper, we propose a probability distribution estimation method based on local probabilistic model assumption. In our method, the estimation of global probability for a certain point is transformed to the computation of local distribution in a small region, where the local distribution is supposed to be simpler and can be assumed as a simpler probabilistic model. By this method, we implement the Bayesian classifiers based on several local probabilistic model assumptions, and experiments with these classifier have been conducted on several real-word biological and medical datasets; the experimental results demonstrate the efficacy of the proposed method for probabilistic classification in medical diagnosis.",

keywords = "Bayesian classification, computer-aided diagnosis, local learning, probabilistic model",

author = "Bin Hu and Chengsheng Mao and Xiaowei Zhang and Yongqiang Dai",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 ; Conference date: 09-11-2015 Through 12-11-2015",

year = "2015",

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doi = "10.1109/BIBM.2015.7359770",

language = "English (US)",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "691--694",

editor = "Schapranow, {lng. Matthieu} and Jiayu Zhou and Hu, {Xiaohua Tony} and Bin Ma and Sanguthevar Rajasekaran and Satoru Miyano and Illhoi Yoo and Brian Pierce and Amarda Shehu and Gombar, {Vijay K.} and Brian Chen and Vinay Pai and Jun Huan",

booktitle = "Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015",

address = "United States",

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Hu, B, Mao, C, Zhang, X & Dai, Y 2015, Bayesian classification with local probabilistic model assumption in aiding medical diagnosis. in LM Schapranow, J Zhou, XT Hu, B Ma, S Rajasekaran, S Miyano, I Yoo, B Pierce, A Shehu, VK Gombar, B Chen, V Pai & J Huan (eds), Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015., 7359770, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Institute of Electrical and Electronics Engineers Inc., pp. 691-694, IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, Washington, United States, 11/9/15. https://doi.org/10.1109/BIBM.2015.7359770

Bayesian classification with local probabilistic model assumption in aiding medical diagnosis. / Hu, Bin; Mao, Chengsheng; Zhang, Xiaowei et al.
Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. ed. / lng. Matthieu Schapranow; Jiayu Zhou; Xiaohua Tony Hu; Bin Ma; Sanguthevar Rajasekaran; Satoru Miyano; Illhoi Yoo; Brian Pierce; Amarda Shehu; Vijay K. Gombar; Brian Chen; Vinay Pai; Jun Huan. Institute of Electrical and Electronics Engineers Inc., 2015. p. 691-694 7359770 (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Bayesian classification with local probabilistic model assumption in aiding medical diagnosis

AU - Hu, Bin

AU - Mao, Chengsheng

AU - Zhang, Xiaowei

AU - Dai, Yongqiang

PY - 2015/12/16

Y1 - 2015/12/16

N2 - In computer-aided diagnosis, a Bayesian classifier that can give the class membership probabilities should be more favorable than classifiers that only give a class assertion. In Bayesian classification, an important and critical step is the probability distribution estimation for each class. Existing methods usually estimate the probability distribution in the whole sample space where the original distribution may be too complex to model. In this paper, we propose a probability distribution estimation method based on local probabilistic model assumption. In our method, the estimation of global probability for a certain point is transformed to the computation of local distribution in a small region, where the local distribution is supposed to be simpler and can be assumed as a simpler probabilistic model. By this method, we implement the Bayesian classifiers based on several local probabilistic model assumptions, and experiments with these classifier have been conducted on several real-word biological and medical datasets; the experimental results demonstrate the efficacy of the proposed method for probabilistic classification in medical diagnosis.

AB - In computer-aided diagnosis, a Bayesian classifier that can give the class membership probabilities should be more favorable than classifiers that only give a class assertion. In Bayesian classification, an important and critical step is the probability distribution estimation for each class. Existing methods usually estimate the probability distribution in the whole sample space where the original distribution may be too complex to model. In this paper, we propose a probability distribution estimation method based on local probabilistic model assumption. In our method, the estimation of global probability for a certain point is transformed to the computation of local distribution in a small region, where the local distribution is supposed to be simpler and can be assumed as a simpler probabilistic model. By this method, we implement the Bayesian classifiers based on several local probabilistic model assumptions, and experiments with these classifier have been conducted on several real-word biological and medical datasets; the experimental results demonstrate the efficacy of the proposed method for probabilistic classification in medical diagnosis.

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KW - local learning

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BT - Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

A2 - Schapranow, lng. Matthieu

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A2 - Hu, Xiaohua Tony

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A2 - Rajasekaran, Sanguthevar

A2 - Miyano, Satoru

A2 - Yoo, Illhoi

A2 - Pierce, Brian

A2 - Shehu, Amarda

A2 - Gombar, Vijay K.

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A2 - Pai, Vinay

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Hu B, Mao C, Zhang X, Dai Y. Bayesian classification with local probabilistic model assumption in aiding medical diagnosis. In Schapranow LM, Zhou J, Hu XT, Ma B, Rajasekaran S, Miyano S, Yoo I, Pierce B, Shehu A, Gombar VK, Chen B, Pai V, Huan J, editors, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 691-694. 7359770. (Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015). doi: 10.1109/BIBM.2015.7359770

Bayesian classification with local probabilistic model assumption in aiding medical diagnosis

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