Predicting asthma outcome using partial least square regression and artificial neural networks
Advances in Artificial Intelligence
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In this paper the potential value of two prognostic factors, namely, bilharziasis status and tumor histological type, is investigated in relation to their abilities to predict disease progression and outcome of patients with bladder cancer, using radial basis function (RBF) neural networks. The bladder cancer data set is described by eight clinical and pathological markers. Two outcomes are of interest: either a patient is alive and free of disease or the patient is dead within five years of diagnosis. Three hundred and twenty-one (321) patients are involved in this retrospective study, 83.5% of whom had been confirmed with bilharziasis history. Selected marker subsets are examined to improve the outcome predictive accuracy and to evaluate the effects of the assessed prognostic factors on such outcome. The highest predictive accuracy for patients with bladder adenocarcinoma, as obtained from the RBF network, is found to be 85% with one subset of markers. The predictive analysis shows that bilharziasis history and patients' histology type are both important prognostic factors in prediction and, for each histology type, different marker combinations with significant characteristics have been observed.