Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches.
In this work we introduce a methodology based on genetic algorithms for the automatic induction of Bayesian networks from a file containing cases and variables related to the problem. The structure is learned by applying three different methods: The Cooper and Herskovits metric for a general Bayesian network, the Markov blanket approach and the relaxed Markov blanket method. The methodologies are applied to the problem of predicting survival of people after 1, 3 and 5 years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained models, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In the four approaches, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.
Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastian, Spain. ccpsiarb@si.ehu.es
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Sierra
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Larrañaga
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003433:Crosses, Genetic; D019992:Databases as Topic; D005103:Expert Systems; D005500:Follow-Up Studies; D005544:Forecasting; D006801:Humans; D008390:Markov Chains; D008545:Melanoma / Q000175:diagnosis* / Q000235:genetics; D009154:Mutation / Q000235:genetics; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000235:genetics; D015996:Survival Rate