Feature selection is one of the most important parts of machine learning. In most datasets in the real world, there might be many features. But not all the features are necessary for a certain machine learning algorithm. Using too much unnecessary features may cause a lot of problems. The first one is definitely the computation cost. The unnecessarily big dataset will take an unnecessarily long time to run the algorithm. At the same time, it may cause an over fitting problem which is not expected at all.The main objective of this proposed research work is to implement hybrid feature selection methods with machine learning classifiers for early prediction of fetal abnormalities with greater accuracy

.Feature selection is one of the most important parts of machine learning. In most datasets in the real world, there might be many features.