“…This, we believe is not exhaustive, other evaluation assessments for its applicability in context will be examined in future research studies. Breast cancer risk prediction Support vector machine accuracy 97.13 [29] Breast cancer diagnosis Support vector machine, Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbors accuracy 97.2 [31] Treatment trend prediction for hypothyrodism Extra trees accuracy 84 [32] Chronic kidney disease prediction Random forest, support vector machine, decision tree accuracy 99.8, 95.5, 98.6 [33] Chronic disease progression (sclerosis) K-nearest neighbor, support vector machine, decision tree, logistic regression auc [34] Chronic kidney disease detection K-nearest neighbor, random forest, neural networks accuracy 0.993 [35] Advanced chronic kidney disease prediction Logistic regression, random forest, decision tree accuracy 81.9, 82.1, 82.1 [36] Prediction of hypertension Deep neural network, decision tree accuracy 0.75, 0.69 [37] Risk prediction (hypertension) k-nearest neighbor, multi-layer perceptron accuracy 82.47 [38] Prediction of hypertension Artificial neural network accuracy 82 [39] Heart disease risk prediction [40] Hypertension prediction extreme Gradient Boosting, Gradient Boosting Machine, Logistic Regression, Random forest, Decision tree and Linear Discriminant Analysis accuracy 83-90% [41] Spam detection Naive Bayes, decision tree, neural networks, random forest, support vector machine accuracy 96.9-99.66 [42] Spam detection ensemble accuracy 99.91 [43] Malicious spam in mails Naive Bayes, support vector machine, logistic regression and random forest accuracy 96.15, 96.15, 98.08, 95.38 [44] Sms spam classification Naive Bayes, BayesNet, C4.5, J48, Self-organizing map and Decision tree accuracy 89.64, 91.11, 80.24, 79.2, 88.24, 75.76 [45] Junk email detection Support vector, random forest accuracy 93.52, 91.41 [46] Email spam detection Bagging, random forest, decision tree (J48) accuracy 98 [47] Credit…”