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Objective: To determine the relationship between existing risk factors in pregnant mothers through data mining techniques. Materials and Methods: A sample of 15,265 women aged between 10 and 48 years, data pertaining to the period between 2010 and 2012, was collected. Data was processed using Microsoft SQL Server 2008 R2 and Microsoft Excel 2007, taking into account the KDD process and CRISP-DM methodology for data mining using k-Means algorithms for Clusters, Association Rules and Correlation Methods. Results: Using Clustering algorithms it was evident that women with a history of abortion form 5% of the study population; women with premature births have a greater presence in abnormal ages being 2% of the population, while suitable age for gestation is only 1%; 1% of women, regardless of age, reported having consumed liquor during pregnancy; women who had abortions and consumed alcohol were 20%; women with induced labor showed 8% alcohol consumption. Women who admitted having used drugs were 2% in ages suitable for gestation and 3% in ages non suitable for conception, placing them in the cluster having the highest probability of abortion.

biosalud

Aplicación de técnicas de minería de datos en atención primaria en salud (APS) para el análisis de riesgos en mujeres gestantes de la población manizaleña atendida por Assbasalud