Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques

Table 1

Summary of the literature survey.

Author	Technique/Model	Datasets	Evaluation measures
Czibula et al. [11]	RAD	MW1, JM1, PC1, PC2, PC3, PC4, KC1, KC3, MC2, and CM1	Accuracy, specificity, precision, PD, and ROC
Li et al. [14]	DP-CNN	Camel, jEdit, Lucene, Xalam, Xerces, Synapse, and Poi	F-measure
Jacob and Raju [15]	HFS, NB, NN, RF, RT, J48	PC1, PC2, PC3, PC4, CM1, MW1, KC3, and JM1	Specificity, sensitivity, MCC, and accuracy
Bashir et al. [16]	NB, RF, KNN, MLP, SVM, J48, and decision stump	CM1, JM1, KC2, MC1, PC1, and PC5	ROC
Miholca et al. [7]	HyGRAR	Tomcat 6.0, Anr 1.7, jEdit 4.0, AR1, jEdit 4.2, AR3, jEdit 4.3, AR5, AR4, and AR6	Accuracy, sensitivity, specificity, and precision
Alsaeedi and Khan [8]	Bagging, SVM, DT, and RF	PC1, PC3, PC4, PC5, JM1, KC2, KC3, MC1, MC2, and CM1	G-measure, specificity, F-score, recall, precision, and accuracy
Iqbal et al. [9]	OneR, NB, K^∗, MLP, SVM, RBF, RF, KNN, DT, and PART	JM1, MW1, CM1, MC1, PC1, MC2, PC4, PC3, PC2, PC5, KC3, and KC1	MCC, ROC area, F-measure, recall, precision, and accuracy
Malhotra and Kamal [6]	J48, RF, NB, AdaBoost, and bagging, and SPIDER3	NASA datasets	Accuracy, sensitivity, specificity, and precision
Manjula and Florence [17]	GA, DNN, NB, RF, DT, ABC, SVM, and KNN	KC1, KC2, CM1, PC1, and JM1	Precision, sensitivity, specificity, recall, F-score, and accuracy