Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data

Abstract

Background/Objectives: Owing to the continuous increase of electronic records and recent advances in machine learning, various automated disease diagnosis tools have been developed and proposed in healthcare sector. In the present study, an ensemble methodology using voting and boosting techniques has been proposed for optimal selection of features and prediction of infants' data of India. Methods/Analysis: For feature selection, the best-first search algorithm of wrapper technique has been used in addition to votingboosting. The proposed ensemble consists of combination of heterogeneous classifiers including Random Forest, J48, JRip, CART and Stochastic Gradient Descent (SGD). The effectiveness of the proposed ensemble and single classifiers have been investigated in terms of classification accuracy, precision, f-measure, recall, MCC and PRC area using varied k-fold cross validation. Findings: The results depicted that the proposed Voting-Boosting ensemble (k=15) outperforms the individual classifiers using selected features. Applications / Improvements: The proposed Voting-Boosting ensemble can be extended by using more state-of-the art classification approaches and further utilized for other healthcare datasets for enhancing the performance.

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“…Recall for any class is defined as the number of correctly predicted positive values out of the total positive values that are true in that particular sample of the class. 64 , 65 It is shown in Equation ( 6 ). …”