Abstract
Bladder cancer is the fourth most common malignancy in men in the western countries. The aim of this study was to develop intelligent systems for invasive bladder cancer progression prediction. The proposed methodology combines knowledge discovery in data using artificial intelligence and knowledge mining. These are used both in feature selection and classifier development. The approach is designed to avoid overfitting and overoptimistic results. To our knowledge, these are the first intelligent systems for prediction of bladder cancer progression, based on boosted C5 decision trees, and their accuracy of 100% is the best published by now.
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Floares, A.G., Luludachi, I., Dinney, C., Adam, L. (2012). Boosted C5 Trees i-Biomarkers Panel for Invasive Bladder Cancer Progression Prediction. In: Biganzoli, E., Vellido, A., Ambrogi, F., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2011. Lecture Notes in Computer Science(), vol 7548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35686-5_16
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DOI: https://doi.org/10.1007/978-3-642-35686-5_16
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