PMID: 16613820Apr 15, 2006Paper

The use of Data Mining in the categorization of patients with Azoospermia

Hormones : International Journal of Endocrinology and Metabolism
Themistoklis MikosJ Papadimas

Abstract

Data Mining is a relatively new field of Medical Informatics. The aim of this study was to compare Data Mining diagnosis with clinical diagnosis by applying a Data Miner (DM) to a clinical dataset of infertile men with azoospermia. One hundred and forty-seven azoospermic men were clinically classified into four groups: a) obstructive azoospermia (n=63), b) non-obstructive azoospermia (n=71), c) hypergonadotropic hypogonadism (n=2), and d) hypogonadotropic hypogonadism (n=11). The DM (IBM's DB2/Intelligent Miner for Data 6.1) was asked to reproduce a four-cluster model. DM formed four groups of patients: a) eugonadal men with normal testicular volume and normal FSH levels (n=86), b) eugonadal men with significantly reduced testicular volume (median 6.5 cm3) and very high FSH levels (n=29), c) eugonadal men with moderately reduced testicular volume (median 14.5 cm3) and raised FSH levels (n=20), and d) hypogonadal men (n=12). Overall DM concordance rate in hypogonadal men was 92%, in obstructive azoospermia 73%, and in non-obstructive azoospermia 69%. Data Mining produces clinically meaningful results but different from those of the clinical diagnosis. It is possible that the use of large sets of structured and formalised data an...Continue Reading

Citations

Apr 10, 2013·Computers in Biology and Medicine·Jun won Lee, Christophe Giraud-Carrier
Feb 3, 2012·Computer Methods and Programs in Biomedicine·Chrystalleni LazarouDemosthenes B Panagiotakos

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