Use of statistical classifiers as support tools for the diagnosis of iron-deficiency anemia in patients on chronic hemodialysis.
Discriminant analysis, logistic regression and neural network models were applied to the diagnosis of iron-deficiency anemia in hemodialyzed patients. The ability of the three quantitative approaches to distinguish between subjects suffering or not from iron-deficiency anemia was compared by re-substitution and cross-validation testing. Methods performance was evaluated by means of sensitivity, specificity and accuracy. All the methods performed globally well (sensitivity and specificity > 0.85), revealing that the problem is classifiable. Neural networks showed the highest accuracy, both in the re-substitution (models developed and tested on the complete data set) and 3-way cross-validation (data set randomly splitted into 3 developmental and validation data sets) testing. These preliminary results suggest that the correct classification of iron status in the hemodialytic population can be treated as a pattern classification problem, for which neural networks and traditional statistical modelling can be a valuable aid to the clinical diagnosis of iron-deficiency anemia. A better performance of the neural network model must be confirmed through prospective testing on a larger data set.
Baiardi
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Medical Informatics Unit, Salvatore Maugeri Foundation, IRCCS, Medical Center of Pavia, Italy.
Piazza
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Montagna
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Mazzoleni
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MC
D018798:Anemia, Iron-Deficiency / Q000145:classification / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011379:Prognosis; D012016:Reference Values; D012044:Regression Analysis; D006435:Renal Dialysis; D012984:Software; D014511:Uremia / Q000097:blood / Q000628:therapy