Predicting the Infertility Treatment Method Using Ensemble Methods and Outlier Analysis

In recent years, the infertility ratio in young couples has been increased a lot in Iran. From the other side, it has been shown that data mining techniques are capable of extracting novel patterns from medical data. In this study, we proposed a comprehensive system called Prediction of the best Infertility treatment using Outlier Detection and Ensemble Methods (PIODEM) for predicting of the best infertility treatment method for infertile couples. This descriptive-correlation study used the information of 527 infertile couples, which collected by Avicenna specialized infertility center, Tehran, Iran. PIODEM consisted of three steps: first, it used the discriminant analysis to find effective factors for choosing the best infertility treatment; second, it detected the outlier samples, and applied a correlation between these samples and the choice of treatment method; third, it used ensemble methods to increase the precision of classifiers. The system of PIODEM succeeded to discover effective factors such as male age, infertility duration, immotile sperm, decreasing of sperm concentration, total sperm count, morphology, sperm motility, sperm with rapid progressive-a motility, and sperm with slow progressive-b motility. Additionally, PIODEM indicated that if one of four features of sperm concentration, toxoplasma immunoglobulin M (IgM), triiodothyronine (T3) hormone, and thyroid peroxidase (TPO) was outlier, then the prediction of treatment would be more accurate. Finally, using ensemble methods increased F-measure of PIODEM up to 76%. The PIODEM system is able to discover effective factors in the choice of treatment method, using differential analysis and analysis of pert data. This system offers patient information as input for the treatment method.