  The aim of this study is to look at predicting whether a person will complete
a drug and alcohol rehabilitation program and the number of times a person
attends. The study is based on demographic data obtained from Substance Abuse
and Mental Health Services Administration (SAMHSA) from both admissions and
discharge data from drug and alcohol rehabilitation centers in Oklahoma.
Demographic data is highly categorical which led to binary encoding being used
and various fairness measures being utilized to mitigate bias of nine
demographic variables. Kernel methods such as linear, polynomial, sigmoid, and
radial basis functions were compared using support vector machines at various
parameter ranges to find the optimal values. These were then compared to
methods such as decision trees, random forests, and neural networks. Synthetic
Minority Oversampling Technique Nominal (SMOTEN) for categorical data was used
to balance the data with imputation for missing data. The nine bias variables
were then intersectionalized to mitigate bias and the dual and triple
interactions were integrated to use the probabilities to look at worst case
ratio fairness mitigation. Disparate Impact, Statistical Parity difference,
Conditional Statistical Parity Ratio, Demographic Parity, Demographic Parity
Ratio, Equalized Odds, Equalized Odds Ratio, Equal Opportunity, and Equalized
Opportunity Ratio were all explored at both the binary and multiclass
scenarios.
