A neural network based breast cancer prognosis model with PCA processed features

Smita Jhajharia; Harish Kumar Varshney; Seema Verma; Rajesh Kumar

doi:10.1109/ICACCI.2016.7732327

A neural network based breast cancer prognosis model with PCA processed features

Smita Jhajharia
, Harish Kumar Varshney
, Seema Verma
, Rajesh Kumar

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

44 Citations (Scopus)

Abstract

Accurate identification of the diagnosed cases is extremely important for a reliable prognosis of breast cancer. Data analytics and learning based methods can provide an effective framework for prognostic studies by accurately classifying data instances into relevant classes based on the tumor severity. Accordingly, a multivariate statistical approach has been coupled with an artificial intelligence based learning technique to implement a prediction model. Principal components analysis pre-processes the data and extracts features in the most relevant form for training an artificial neural network that learns the patterns in the data for classification of new instances. The diagnostic data of the original Wisconsin breast cancer database accessed from the UCI machine learning repository has been used in the study. The proposed hybrid model shows promising results when compared with other classification algorithms used most commonly in the literature and can provide a future scope for creation of more sophisticated machine learning based cancer prognostic models.

Original language	English
Title of host publication	2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016
Editors	Joel J. P. C. Rodrigues, Patrick Siarry, Gregorio Martinez Perez, Raghuvir Tomar, Al-Sakib Khan Pathan, Sameep Mehta, Sabu M. Thampi, Stefano Berretti, Ravi Prakash Gorthi, Al-Sakib Khan Pathan, Jinsong Wu, Jie Li, Vivek Jain, Joel J. P. C. Rodrigues, Mohammed Atiquzzaman, Joel J. P. C. Rodrigues, Punam Bedi, Mohamed Habib Kammoun
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1896-1901
Number of pages	6
ISBN (Electronic)	9781509020287
DOIs	https://doi.org/10.1109/ICACCI.2016.7732327
Publication status	Published - 2 Nov 2016
Externally published	Yes
Event	5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016 - Jaipur, India Duration: 21 Sept 2016 → 24 Sept 2016

Publication series

Name	2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016

Conference

Conference	5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016
Country/Territory	India
City	Jaipur
Period	21/09/16 → 24/09/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial Neural Network
Classification
Prediction model
Principal Component Analysis
Prognosis

ASJC Scopus subject areas

Information Systems
Computer Science (miscellaneous)
Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1109/ICACCI.2016.7732327

Cite this

Jhajharia, S., Varshney, H. K., Verma, S., & Kumar, R. (2016). A neural network based breast cancer prognosis model with PCA processed features. In J. J. P. C. Rodrigues, P. Siarry, G. M. Perez, R. Tomar, A.-S. K. Pathan, S. Mehta, S. M. Thampi, S. Berretti, R. P. Gorthi, A.-S. K. Pathan, J. Wu, J. Li, V. Jain, J. J. P. C. Rodrigues, M. Atiquzzaman, J. J. P. C. Rodrigues, P. Bedi, & M. H. Kammoun (Eds.), 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016 (pp. 1896-1901). Article 7732327 (2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2016.7732327

Jhajharia, Smita ; Varshney, Harish Kumar ; Verma, Seema et al. / A neural network based breast cancer prognosis model with PCA processed features. 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016. editor / Joel J. P. C. Rodrigues ; Patrick Siarry ; Gregorio Martinez Perez ; Raghuvir Tomar ; Al-Sakib Khan Pathan ; Sameep Mehta ; Sabu M. Thampi ; Stefano Berretti ; Ravi Prakash Gorthi ; Al-Sakib Khan Pathan ; Jinsong Wu ; Jie Li ; Vivek Jain ; Joel J. P. C. Rodrigues ; Mohammed Atiquzzaman ; Joel J. P. C. Rodrigues ; Punam Bedi ; Mohamed Habib Kammoun. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1896-1901 (2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016).

@inproceedings{a590e4d236a84ff4abedbacfa96322df,

title = "A neural network based breast cancer prognosis model with PCA processed features",

abstract = "Accurate identification of the diagnosed cases is extremely important for a reliable prognosis of breast cancer. Data analytics and learning based methods can provide an effective framework for prognostic studies by accurately classifying data instances into relevant classes based on the tumor severity. Accordingly, a multivariate statistical approach has been coupled with an artificial intelligence based learning technique to implement a prediction model. Principal components analysis pre-processes the data and extracts features in the most relevant form for training an artificial neural network that learns the patterns in the data for classification of new instances. The diagnostic data of the original Wisconsin breast cancer database accessed from the UCI machine learning repository has been used in the study. The proposed hybrid model shows promising results when compared with other classification algorithms used most commonly in the literature and can provide a future scope for creation of more sophisticated machine learning based cancer prognostic models.",

keywords = "Artificial Neural Network, Classification, Prediction model, Principal Component Analysis, Prognosis",

author = "Smita Jhajharia and Varshney, \{Harish Kumar\} and Seema Verma and Rajesh Kumar",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016 ; Conference date: 21-09-2016 Through 24-09-2016",

year = "2016",

month = nov,

day = "2",

doi = "10.1109/ICACCI.2016.7732327",

language = "English",

series = "2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016",

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

pages = "1896--1901",

editor = "Rodrigues, \{Joel J. P. C.\} and Patrick Siarry and Perez, \{Gregorio Martinez\} and Raghuvir Tomar and Pathan, \{Al-Sakib Khan\} and Sameep Mehta and Thampi, \{Sabu M.\} and Stefano Berretti and Gorthi, \{Ravi Prakash\} and Pathan, \{Al-Sakib Khan\} and Jinsong Wu and Jie Li and Vivek Jain and Rodrigues, \{Joel J. P. C.\} and Mohammed Atiquzzaman and Rodrigues, \{Joel J. P. C.\} and Punam Bedi and Kammoun, \{Mohamed Habib\}",

booktitle = "2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016",

address = "United States",

}

Jhajharia, S, Varshney, HK, Verma, S & Kumar, R 2016, A neural network based breast cancer prognosis model with PCA processed features. in JJPC Rodrigues, P Siarry, GM Perez, R Tomar, A-SK Pathan, S Mehta, SM Thampi, S Berretti, RP Gorthi, A-SK Pathan, J Wu, J Li, V Jain, JJPC Rodrigues, M Atiquzzaman, JJPC Rodrigues, P Bedi & MH Kammoun (eds), 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016., 7732327, 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1896-1901, 5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016, Jaipur, India, 21/09/16. https://doi.org/10.1109/ICACCI.2016.7732327

A neural network based breast cancer prognosis model with PCA processed features. / Jhajharia, Smita; Varshney, Harish Kumar; Verma, Seema et al.
2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016. ed. / Joel J. P. C. Rodrigues; Patrick Siarry; Gregorio Martinez Perez; Raghuvir Tomar; Al-Sakib Khan Pathan; Sameep Mehta; Sabu M. Thampi; Stefano Berretti; Ravi Prakash Gorthi; Al-Sakib Khan Pathan; Jinsong Wu; Jie Li; Vivek Jain; Joel J. P. C. Rodrigues; Mohammed Atiquzzaman; Joel J. P. C. Rodrigues; Punam Bedi; Mohamed Habib Kammoun. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1896-1901 7732327 (2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016).

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

TY - GEN

T1 - A neural network based breast cancer prognosis model with PCA processed features

AU - Jhajharia, Smita

AU - Varshney, Harish Kumar

AU - Verma, Seema

AU - Kumar, Rajesh

PY - 2016/11/2

Y1 - 2016/11/2

N2 - Accurate identification of the diagnosed cases is extremely important for a reliable prognosis of breast cancer. Data analytics and learning based methods can provide an effective framework for prognostic studies by accurately classifying data instances into relevant classes based on the tumor severity. Accordingly, a multivariate statistical approach has been coupled with an artificial intelligence based learning technique to implement a prediction model. Principal components analysis pre-processes the data and extracts features in the most relevant form for training an artificial neural network that learns the patterns in the data for classification of new instances. The diagnostic data of the original Wisconsin breast cancer database accessed from the UCI machine learning repository has been used in the study. The proposed hybrid model shows promising results when compared with other classification algorithms used most commonly in the literature and can provide a future scope for creation of more sophisticated machine learning based cancer prognostic models.

AB - Accurate identification of the diagnosed cases is extremely important for a reliable prognosis of breast cancer. Data analytics and learning based methods can provide an effective framework for prognostic studies by accurately classifying data instances into relevant classes based on the tumor severity. Accordingly, a multivariate statistical approach has been coupled with an artificial intelligence based learning technique to implement a prediction model. Principal components analysis pre-processes the data and extracts features in the most relevant form for training an artificial neural network that learns the patterns in the data for classification of new instances. The diagnostic data of the original Wisconsin breast cancer database accessed from the UCI machine learning repository has been used in the study. The proposed hybrid model shows promising results when compared with other classification algorithms used most commonly in the literature and can provide a future scope for creation of more sophisticated machine learning based cancer prognostic models.

KW - Artificial Neural Network

KW - Classification

KW - Prediction model

KW - Principal Component Analysis

KW - Prognosis

UR - https://www.scopus.com/pages/publications/85007407195

U2 - 10.1109/ICACCI.2016.7732327

DO - 10.1109/ICACCI.2016.7732327

M3 - Conference contribution

AN - SCOPUS:85007407195

T3 - 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016

SP - 1896

EP - 1901

BT - 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016

A2 - Rodrigues, Joel J. P. C.

A2 - Siarry, Patrick

A2 - Perez, Gregorio Martinez

A2 - Tomar, Raghuvir

A2 - Pathan, Al-Sakib Khan

A2 - Mehta, Sameep

A2 - Thampi, Sabu M.

A2 - Berretti, Stefano

A2 - Gorthi, Ravi Prakash

A2 - Pathan, Al-Sakib Khan

A2 - Wu, Jinsong

A2 - Li, Jie

A2 - Jain, Vivek

A2 - Rodrigues, Joel J. P. C.

A2 - Atiquzzaman, Mohammed

A2 - Rodrigues, Joel J. P. C.

A2 - Bedi, Punam

A2 - Kammoun, Mohamed Habib

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016

Y2 - 21 September 2016 through 24 September 2016

ER -

Jhajharia S, Varshney HK, Verma S, Kumar R. A neural network based breast cancer prognosis model with PCA processed features. In Rodrigues JJPC, Siarry P, Perez GM, Tomar R, Pathan ASK, Mehta S, Thampi SM, Berretti S, Gorthi RP, Pathan ASK, Wu J, Li J, Jain V, Rodrigues JJPC, Atiquzzaman M, Rodrigues JJPC, Bedi P, Kammoun MH, editors, 2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1896-1901. 7732327. (2016 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2016). doi: 10.1109/ICACCI.2016.7732327

A neural network based breast cancer prognosis model with PCA processed features

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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