## 3 Results
\begin{table}
\begin{tabular}{l l c} \hline \hline \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Value**} & \multicolumn{1}{c}{**Number (\%)**} \\ \hline \multirow{6}{*}{Previous substance use treatment episodes} & No prior treatment episodes & 39,112 (29.6) \\  & One prior treatment episode & 28,334 (21.4) \\  & Five or more prior treatment episodes & 24,478 (18.5) \\ (NOPRIOR) & Two prior treatment episodes & 19,212 (14.5) \\  & Three prior treatment episodes & 13,101 (9.9) \\  & Four prior treatment episodes & 7891 (6) \\ \hline \multirow{6}{*}{Type of treatment service/setting (SERVICES)} & Ambulatory, non-intensive outpatient & 70,284 (53.2) \\  & Rehab/residential, short-term (30 days or fewer) & 21,110 (16) \\  & Ambulatory, intensive outpatient & 15,802 (12) \\  & Detox, 24-h, free-standing residential & 13,945 (10.6) \\  & Other & 10,983 (8.4) \\ \hline \multirow{6}{*}{Referral source (PSOURCE)} & Individual (includes self-referral) & 62,232 (47.1) \\  & Court/criminal justice referral/DUI/DWI & 29,059 (22) \\ \cline{1-1}  & Other community referral & 15,135 (11.5) \\ \cline{1-1}  & Alcohol/drug use care provider & 14,176 (10.7) \\ \cline{1-1}  & Other & 11,535 (9.7) \\ \hline \multirow{6}{*}{Race (RACE)} & Black or African American & 97,677 (73.9) \\  & Asian or Pacific Islander & 20,931 (15.8) \\ \cline{1-1}  & White & 8788 (6.7) \\ \cline{1-1}  & Other & 4732 (3.5) \\ \hline \multirow{6}{*}{Substance use (secondary) (SUB2)} & None & 48,513 (36.7) \\  & Cocaine/crack & 19,126 (14.5) \\ \cline{1-1}  & Marijuana/hashish & 18,044 (13.7) \\ \cline{1-1}  & Methamphetamine/speed & 11,659 (8.8) \\ \cline{1-1}  & Alcohol & 10,974 (8.3) \\ \cline{1-1}  & Other opiates and synthetics & 7012 (5.3) \\ \cline{1-1}  & Other & 10,246 (12.7) \\ \hline \multirow{6}{*}{Substance use (tertiary) (SUB3)} & None & 92,205 (69.8) \\ \cline{1-1}  & Marijuana/hashish & 10,689 (8.1) \\ \cline{1-1}  & Alcohol & 6454 (4.9) \\ \cline{1-1}  & Cocaine/crack & 5465 (4.1) \\ \cline{1-1}  & Methamphetamine/speed & 3373 (2.6) \\ \cline{1-1}  & Other & 13,942 (10.5) \\ \hline \multirow{6}{*}{Employment (EMPLOY)} & Unemployed & 69,048 (52.3) \\ \cline{1-1}  & Not in labour force & 51,075 (38.7) \\ \cline{1-1}  & Part-time & 12,005 (9.1) \\ \hline \multirow{6}{*}{Diagnostic and Statistical Manual of Mental Disorders diagnosis (DSMCRIT)} & Opioid dependence & 44,699 (33.8) \\ \cline{1-1}  & Alcohol dependence & 22,077 (16.7) \\ \cline{1-1}  & Other substance dependence & 17,766 (13.4) \\ \cline{1-1}  & Other mental health condition & 14,540 (11) \\ \cline{1-1}  & Cannabis dependence & 6567 (5) \\ \cline{1-1}  & Cocaine dependence & 6196 (4.7) \\ \cline{1-1}  & Other & 20,283 (15.4) \\ \hline \multirow{2}{*}{Medication-assisted opioid therapy (METHUSE)} & Yes & 34,807 (26.3) \\ \cline{1-1}  & No & 97,321 (73.7) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._followed by XGBoost with an AUC of 0.809. Several ensemble techniques such as Random Forest (AUC = 0.807) and Extra Trees (AUC = 0.803) showed significant discriminatory ability, closely following the top-performing algorithm. Meanwhile, conventional methods such as Gradient-Boosted Trees (AUC = 0.799) and Single-Layer Perceptron (AUC = 0.776) demonstrated comparable but slightly lower AUC scores. Nevertheless, the utilisation of K Nearest Neighbors (grid) resulted in a relatively reduced prediction accuracy, as indicated by an AUC of 0.670. Interestingly, models employing Deep Neural Network architecture exhibited the least satisfactory performance among the investigated algorithms, achieving an AUC of 0.631. These findings highlight the superiority of the proposed ensemble-based approach in achieving higher AUC values and therefore more successful binary classification in this experimental environment. Table 3 provides the mean and standard deviation (SD) for each metric across the different models.