TY - GEN
T1 - Metastasis identification based on clinical parameters using Bayesian network
AU - Syafiandini, Arida Ferti
AU - Wasito, Ito
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/19
Y1 - 2016/9/19
N2 - Bayesian Network is a promising method for modelling probabilistic relationships among causally related variables. This paper presents an application of Bayesian Network for identifying the occurrence of metastasis in a patient with positive or negative breast cancer tumor based on observed clinical parameters. Its structure is built using K2 search algorithm with a topological order obtained from Minimum Weight Spanning Tree. Maximum Likelihood Estimation is also employed to learn parameter (prior and conditional probabilities) from network structure. For metastasis identification, the marginal probability is computed using Junction Inference Tree. Compared to Logistic Regression and Linear Discriminant Analysis, Bayesian Network gives a clearer idea about how each clinical parameter relates to another. In terms of average accuracy, sensitivity, and selectivity, Bayesian Network also outperforms those methods.
AB - Bayesian Network is a promising method for modelling probabilistic relationships among causally related variables. This paper presents an application of Bayesian Network for identifying the occurrence of metastasis in a patient with positive or negative breast cancer tumor based on observed clinical parameters. Its structure is built using K2 search algorithm with a topological order obtained from Minimum Weight Spanning Tree. Maximum Likelihood Estimation is also employed to learn parameter (prior and conditional probabilities) from network structure. For metastasis identification, the marginal probability is computed using Junction Inference Tree. Compared to Logistic Regression and Linear Discriminant Analysis, Bayesian Network gives a clearer idea about how each clinical parameter relates to another. In terms of average accuracy, sensitivity, and selectivity, Bayesian Network also outperforms those methods.
KW - Bayesian Network
KW - clinical parameters
KW - metastasis
UR - http://www.scopus.com/inward/record.url?scp=84992145803&partnerID=8YFLogxK
U2 - 10.1109/ICoICT.2016.7571919
DO - 10.1109/ICoICT.2016.7571919
M3 - Conference contribution
AN - SCOPUS:84992145803
T3 - 2016 4th International Conference on Information and Communication Technology, ICoICT 2016
BT - 2016 4th International Conference on Information and Communication Technology, ICoICT 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Information and Communication Technology, ICoICT 2016
Y2 - 25 May 2016 through 27 May 2016
ER -