Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.

ABSTRACT: New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients d

SUBMITTER: Gongora Alonso S 

PROVIDER: S-EPMC9003328 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature