Evaluating Prediction Performance of Link Functions in Diagnosis Models for Alcohol Addiction

Abstract

Since diagnosis of alcohol addiction is largely questionnaire-based, the information communication technology (ICT)-driven advancement of big data application enables development of mathematical models for more effective diagnosis of addiction. This study presents a prototype diagnostic mathematical model for an interdisciplinary alcohol addiction diagnostic system under construction. Survey data acquired from 253 subjects using the Korean alcohol addiction test developed by the Korean National Mental Health Center was utilized to develop a mathematical model based on ordinal logistic analysis which defines the degree of alcohol addiction as probability. Because the type of link function determines the model’s accuracy, five types of link functions such as logit function, cauchit function, complementary log-log function, negative log-log function, and probit function were used to develop five types of diagnostic mathematical models. These models were then assessed for accuracy using prediction accuracy, probability of detection, and false alarm rate tests to select an optimal model for alcohol addiction diagnosis. Our study shows the resistance distribution of alcoholism is similar to the Gumbel distribution, and a model which uses the complementary log-log function is the most suitable one for the diagnosis of alcoholism.