Geographical Clustering of Cancer Incidence by Means of Bayesian Networks and Conditional Gaussian Networks

José M. Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:237-242, 2001.

Abstract

With the aim of improving knowledge on the geographical distribution and characterization of malignant tumors in the Autonomous Community of the Basque Country (Spain), age-standardized cancer incidence rates of the 6 most frequent cancer types for patients of each sex between 1986 and 1994 are analyzed, in relation to the towns of the Community. Concretely, we perform a geographical clustering of the towns of the Community by means of Bayesian networks and conditional Gaussian networks. We present several maps that show the clusterings encoded by the learnt models. In addition to this, we outline the cancer incidence profile for each of the obtained clusters.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR3-pena01a, title = {Geographical Clustering of Cancer Incidence by Means of {B}ayesian Networks and Conditional {G}aussian Networks}, author = {Pe{\~{n}}a, Jos{\'{e}} M. and Izarzugaza, I. and Lozano, Jos{\'{e}} Antonio and Aldasoro, E. and Larra{\~{n}}aga, Pedro}, booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics}, pages = {237--242}, year = {2001}, editor = {Richardson, Thomas S. and Jaakkola, Tommi S.}, volume = {R3}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r3/pena01a/pena01a.pdf}, url = {https://proceedings.mlr.press/r3/pena01a.html}, abstract = {With the aim of improving knowledge on the geographical distribution and characterization of malignant tumors in the Autonomous Community of the Basque Country (Spain), age-standardized cancer incidence rates of the 6 most frequent cancer types for patients of each sex between 1986 and 1994 are analyzed, in relation to the towns of the Community. Concretely, we perform a geographical clustering of the towns of the Community by means of Bayesian networks and conditional Gaussian networks. We present several maps that show the clusterings encoded by the learnt models. In addition to this, we outline the cancer incidence profile for each of the obtained clusters.}, note = {Reissued by PMLR on 31 March 2021.} }
Endnote
%0 Conference Paper %T Geographical Clustering of Cancer Incidence by Means of Bayesian Networks and Conditional Gaussian Networks %A José M. Peña %A I. Izarzugaza %A José Antonio Lozano %A E. Aldasoro %A Pedro Larrañaga %B Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2001 %E Thomas S. Richardson %E Tommi S. Jaakkola %F pmlr-vR3-pena01a %I PMLR %P 237--242 %U https://proceedings.mlr.press/r3/pena01a.html %V R3 %X With the aim of improving knowledge on the geographical distribution and characterization of malignant tumors in the Autonomous Community of the Basque Country (Spain), age-standardized cancer incidence rates of the 6 most frequent cancer types for patients of each sex between 1986 and 1994 are analyzed, in relation to the towns of the Community. Concretely, we perform a geographical clustering of the towns of the Community by means of Bayesian networks and conditional Gaussian networks. We present several maps that show the clusterings encoded by the learnt models. In addition to this, we outline the cancer incidence profile for each of the obtained clusters. %Z Reissued by PMLR on 31 March 2021.
APA
Peña, J.M., Izarzugaza, I., Lozano, J.A., Aldasoro, E. & Larrañaga, P.. (2001). Geographical Clustering of Cancer Incidence by Means of Bayesian Networks and Conditional Gaussian Networks. Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R3:237-242 Available from https://proceedings.mlr.press/r3/pena01a.html. Reissued by PMLR on 31 March 2021.

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