Prediction of Cocaine Inpatient Treatment Success Using Machine Learning on High-Dimensional Heterogeneous Data

Jose Tapia-Galisteo,Diego Urgeles Puertolas,Francisco J Izquierdo,Gema Garcia-Saez,Jose M Iniesta,M Elena Hernando,Carmen Perez-Gandia

doi:10.1109/access.2020.3041895

Abstract

The high prevalence of drug addiction is a major health challenge that pressures healthcare systems to respond with cost-effective treatments. To improve the treatment success of drug-dependent patients, it is necessary to identify the main associated risk factors for dropping out of treatment. Previous research shows disparate results due to the wide variety of approaches employed, the different and/or poorly defined metrics used, and the different target populations under study. This article presents the design and selection of a predictive model to estimate success of inpatient cocaine treatment based on a high-dimensional heterogeneous set of characteristics, with the aim of learning new associations between independent characteristics. We evaluated different feature selection techniques and machine learning algorithms to design the best predictive model in terms of accuracy, area under the receiver operating characteristic curve, recall, specificity, F1-score, and Matthews correlation coefficient. Random Forest was the top-performing model with a characteristic set consisting of 11 features selected with a wrapper evaluator and the Best First algorithm, achieving 82% accuracy, 0.81 of area under the receiver operating characteristic curve, 0.96 of recall, 0.47 of specificity, 0.89 of F1-measure and 0.53 of Matthews correlation coefficient. The predictive model’s performance was enhanced by combining multiple dimensions with variables referring to previous treatments, mental exploration, cognitive functioning, personality, consumption habits, and pharmacological treatment. We have refined the use of machine learning techniques to predict drug addiction treatment success, which could represent a new step in treatment management especially when included in clinical decision support systems.

Highlights

The high prevalence of cocaine addiction is a major health challenge that leads to pressure on healthcare systems to respond with cost-effective treatments
We evaluated 4 Machine learning (ML) algorithms (RF, logistic regression (LR), multilayer perceptron (MLP) neuronal network, and Support Vector Machine (SVM)) that are widely used in predicting therapeutic outcomes in drug addiction and psychiatry, in combination with 3 different methods of feature selection
FEATURE SELECTION Table V shows to which extent a feature was selected for any combination of ML algorithm and feature selection strategy

Summary

Introduction

The high prevalence of cocaine addiction is a major health challenge that leads to pressure on healthcare systems to respond with cost-effective treatments. According to a recent systematic review about the applications of ML in addiction studies, previous research works could be divided according to the type of addiction, differentiating between smoking cigarettes, alcohol, cocaine, opioids, multiple substance use, internet addiction, and game addiction. Focusing on the papers on drug use, this review includes 14 papers published between 2012 and 2018, most of them developed in North America and to a lesser extent in Europe and Asia, and with sample sizes ranging from 22 to 228,405 subjects [31]. The subtypes of ML methods used in these research works are classification, regression, ensemble, multiple comparison of algorithms, clustering, and direct reinforcement learning, with most being supervised learning methods. Model evaluation methods included k-fold crossvalidation, the receiver operating characteristic (ROC) curve, chi-squared test, leave-one-out cross-validation, variance analysis and multiple comparisons with Bonferroni correction

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