 We then use and compare the predictive power of four well-known machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the risk of such dependency. Results showed that the random forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings.
<clarity>  We then use and compare the predictive power of four well-known machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the risk of such dependency. Results showed that the random forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings.
 We then use and compare the predictive power of four well-known machine learning algorithms: logistic regression, random forest, decision tree, and gradient boosting, to predict the risk of opioid use disorder. The study results show that the Random Forest model outperforms the other three algorithms while determining the features, some of which are consistent with prior clinical findings.