Predicting Male Fertility Using Soft Computing Approach
There has been a dramatic decrease in fertility over the last decade and Studies indicate that 30% of infertility is
related to male factor; with environmental factors and lifestyle habits having an impact on male fertility. This is contrary to
cultural believed that infertility is often a woman's problem. In this study we employ a soft computing approach using
ANFIS model to predict semen quality by considering environmental factors, lifestyle habits and certain diseases. 100
instances with 10 attributes from datasets obtained from the UCI machine learning repository. 70% of the data sets were used
for training the model while 30% for testing the model which was simulated in Matlab environment. The results from this
study gave 90% classification accuracy in predicting male fertility.
Keywords- Semen quality, Prediction, Soft computing, Anfis, Machine learning.