Feature Reduction by Improvised Hybrid Algorithm for Predicting the IVF Success Rate
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Abstract
The most common problem nowadays is Infertility which is caused by different factors like environment, genetic or personal characteristics. IVF, IUI are some of the different treatments available to overcome the problem but the cost and emotion beyond every cycle is different which affects the success rate of the treatment. So Data Mining techniques are suggested as good tools for predicting the success rate of IVF treatment. The quality of research or knowledge obtained from the data set depends upon the data. If the data set contains irrelevant or noisy data there is possibility for decrease in the knowledge gained from it. This paper proposes an Improvised hybrid algorithm which combines the existing Ant Colony and Relative Reduct algorithm for Pre-Processing. In this work, the proposed Algorithm is compared with the existing related algorithms. It is evident from the results that the proposed algorithm achieved its target of reducing the features to minimum numbers without compromising the core knowledge of the system to estimate the success rate.
Keywords: Ant Colony, Relative Reduct Algorithm, Success Rate, Feature Reduction, Accuracy
Keywords: Ant Colony, Relative Reduct Algorithm, Success Rate, Feature Reduction, Accuracy
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