01322nas a2200193   4500000000100000000000100001008004100002260001200043653001900055653003100074100001200105700001600117245008400133856007400217300000900291490000600300520080800306022001401114       2010                            d  c12/201010aClassification10aArtificial Neural Networks1 aG Rumbe1 aHaowen Youh00aComparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data  uhttp://www.ijimai.org/journal/sites/default/files/IJIMAI20101_3_1.pdf  a5-120 v13 aAccurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem.  a1989-1660