Sensors (Basel, Switzerland), 2022-03, Vol.22 (7), p.2517
2022
Volltextzugriff (PDF)
Details
- Autor(en) / Beteiligte
- Titel
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia
- Ist Teil von
- Sensors (Basel, Switzerland), 2022-03, Vol.22 (7), p.2517
- Ort / Verlag
- Switzerland: MDPI AG
- Erscheinungsjahr
- 2022
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- 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 diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1424-8220eISSN: 1424-8220DOI: 10.3390/s22072517Titel-ID: cdi_doaj_primary_oai_doaj_org_article_58382c81a9504a5480d16601c0afcc8a
- Format
- –
- Schlagworte
- Accuracy, Algorithms, Artificial intelligence, Bayes Theorem, Coding standards, Data mining, Datasets, Decision making, Decision trees, Disease, Feature selection, Health risk assessment, Hospitalization, Humans, Internet of Things, Machine Learning, machine learning algorithms, Medical research, Mental disorders, Mental health, Mental health care, Patients, Prediction models, predictive models, random forest, Schizophrenia, Schizophrenia - diagnosis, Support Vector Machine, Support vector machines