Fertility Analysis Method Based on Supervised and Unsupervised Data Mining Techniques

摘要

The fertility potential analysis represents a research topic of great interest, and could help us to understand all the factors that difficult to have high fertility rates, which its quite relevant to be able to propose solutions and obtain an increase in the fertility levels especially for men. Data mining techniques are widely used for many authors studying this condition, and the metrics to evaluate results using these techniques usually are: accuracy, coverage, positive false and negative false rate. In this study we implemented the following data mining techniques: Decision Trees, Support Vector Machines, Bayesian Networks and K-Nearest Neighbor, and obtained high levels in all metrics, so we can conclude that our method proposal represents a tool to support decision making for patient analysis with fertility problems.