An Ensemble Based Model for Detecting Genetically Inherited Disorder

An Ensemble of Classifiers is a series of skilled classifier models whose predictions are blended to attain an ultimate decision. It is a technique to choose for prediction from the multidimensional data which can improve the classification accuracy in diverse datasets. In addition, the best method that can reduce error arising from single classifier i.e., over fitting, inaccuracy and at times complexity of train a model. Also, most individual classifiers are not suitable for large data sets and their performance tend to be slow. This study is based on Ensemble algorithms namely Voting, Stacking and Bagging by evaluations which are performed using different classifiers. Data set from Data world digest containing different attributes and dimensions will be used for the study. This study shows that detection of disorder based on ensemble model obtains better results in average accuracy of eighty three percent than other methods. The benefit of the model is an increased accuracy and a reduced run-time when performing the disorder detection.