title: Similarity classifier in diagnosis of bladder cancer.
abstract: In this article a similarity classifier's performance is studied in the diagnosis of bladder cancer. It is demonstrated that good classification results in diagnosis of bladder cancer are already achieved with a very small amount of data in the training set with the use of similarity classifier. When a new disease is initially discovered, the amount of samples are always quite limited (due to a fact that amount of patients is few), and this situation makes clinical work very difficult. A similarity classifier is a fast and accurate tool for medical diagnosis and it is capable of accurate performance already with a limited amount of data. This is quite important because there is a very limited amount of techniques available even to deal with such small sample sizes and especially in the diagnosis of cancer, high diagnosis accuracy is most important. In this study similarity classifier is used in diagnosis of bladder cancer. A good accuracy (of 100%) is already achieved with very small amount of samples in training the classifier. Here only four samples (two persons with bladder cancer and two persons without bladder cancer) were needed to train classifier managing the diagnosis of bladder cancer with 100% accuracy.
authors.affiliation: Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finland. pasi.luukka@lut.fi
authors.forename: Pasi
authors.initials: P
authors.lastname: Luukka
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D016571:Neural Networks, Computer; D001749:Urinary Bladder Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics