A Study on Fertility Data using Data Mining Techniques

An integral part of human beings is reproduction, which is dependent on fertility rates. The focus of the study here is to study the male fertility rates and classify them into a decision making process. Use the following J48, Random Forest and LAD algorithms are used for classification of data sets. The datasets are classified on the basis of the accuracy, error rates like RMSE, MAE etc. and finally predictions are done. The model predicts the fertility class for the input test data. Thus this is a helpful and useful tool in the health industry which will make accurate predictions by increasing prediction rates and helps in making preventive decisions. This enables persons to be easily identified well in advance with good fertility rates or having other defects. Thus the study using data mining techniques helps in the identification of the dataset population to predict the fertility rates and find which method is more accurate or suitable for predicting the fertility of the population in general with the available data.