Table 1.

Review of machine learning for disease prediction

References	Algorithm	Key results
[4]	Logistic Regression, Random Forests	0.72 AUC
[11]	Model Ensembles	0.80 AUC
[51]	Random Forests	0.92 AUC
[3]	Random Forests	0.82 AUC
[1]	Several	0.69–0.97 AUC
[19]	Random Forests	59–$80\%$ Precision
[32]	Several	$99.3\%$ specificity
[8]	Random Forests, SVM and others	92–$95\%$ sensitivity
[12]	LSTM	62–$87\%$ accuracy
[13]	DNNs	96–$98\%$ accuracy
[27]	Naïve Bayes	$96.20\%$ accuracy
[25]	Several	–
[29]	Several	–
[15]	Several	–
This article	xgBoost + Bayesian Optimization	0.94 AUC