Data Mining Models Applied in Prediction of IVF Success Rates: An Overview
IVF success rates can be increased by the analysis of multiple factors related to the process. The automated systems which take these multiple factors as input and produce prediction as output are the need of the hour in IVF research area. The automated systems include mechanism for pre processing of input metrics and prediction stage that is built using algorithms such as decision trees, naive Bayes classifiers, case based reasoning system, logistic regression, etc. In recent years, Artificial Neural Networks (ANN) and hybrid methods are also in use for IVF success rate prediction. There exists lot of literature in the area of IVF success rate prediction. Analyzing the works done so far can help the researchers to identify the research gap and work toward more efficient methods. This work is aimed to present a detailed review of existing techniques applied for the prediction of IVF success rates. Various techniques used so far have been discussed in detail. The drawback of each of the methods is identified which provides the key for future researches in this area. This area has been chosen considering the challenge and social relevance.