An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems

Abstract

The healthcare systems are extensively being used with increased focus on safety of patients. Software engineering for healthcare applications is an emerging research area. Detecting defects is a critical step of software development process of healthcare applications. The performance of the Software Defect Prediction model (SDP) depends on the features of healthcare system; irrelevant features decrease the performance of the model. An optimized feature selection technique is needed to recognize and remove the irrelevant features. In this study, a new optimized feature selection technique, i.e., multiobjective Harris Hawk Optimization (HHO), is proposed for binary classification problem with Adaptive Synthetic Sampling (ADASYN) Technique. Multiobjective HHO is proposed with two main objectives, one to reduce the total amount of selected features and the other to maximize the performance of the proposed model. The multiobjective feature selection technique helps to find the optimal solution to achieve the desired objectives and increase the classification performance in terms of accuracy, AUC, precision, recall, and F1-measure. The study conducts an experiment on a healthcare dataset. Six different search techniques (RF, SVM, bagging, adaptive boosting, voting, and stacking) are implemented on the dataset. The proposed model helps to predict the software defects with a significant classification accuracy of 0.990 and AUC score of 0.992.