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
N1 - Publisher Copyright:
© 2016 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85007407195&partnerID=8YFLogxK
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 -