Comparison of machine learning techniques for the classification of patients with mental and behavioral disorders due to psychotropic consumption in the city of Barranquilla
DOI:
https://doi.org/10.61799/2216-0388.634Keywords:
machinelearning, support vector machine, random forest, artificial neural network, substance psychoactiveAbstract
Substance use disorder contributes to a substantial global burden of disease, despite ongoing efforts by government entities to mitigate this problem. This problem is one of the most attractive current research areas for developing machine learning models. This research study aimed to develop a Machine Learning model for the classification of patients with mental and behavioral disorders due to the consumption of psychotropic substances located in the acute intoxication class or dependency syndrome in the city of Barranquilla. The method used was to train, validate and compare four Machine Learning techniques with databases of patients in Barranquilla. The results revealed that Random Forest and Logistic Regression had the best accuracy (72%). However, Artificial Neural Network is the best model to predict the proportion of positive cases among the detected positive cases. On the other hand, the best predictor of the proportion of positive cases that are well detected is Random Forest, and the best predictor of the proportion of negative cases that are well detected is the Support Vector Machine. Finally, it is worth mentioning that Artificial Neural Network and Random Forest are the best classifiers that AUC records with 80% each. In general terms, Artificial Neural Network and Random Forest showed signs of being a good classifier to discriminate between patients who would potentially be in a case of acute intoxication or dependency syndrome, obtaining average performance values between 80% and 90%.
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