Predictive Modeling of Hospital Readmission of Schizophrenic Patients in a Spanish Region Combining Particle Swarm Optimization and Machine Learning Algorithms.

1. Góngora Alonso, Susel ³
2. Herrera Montano, Isabel ³
3. De la Torre Díez, Isabel ³
4. Franco-Martín, Manuel ¹
5. Amoon, Mohammed ²
6. Román-Gallego, Jesús-Angel ¹
7. Pérez-Delgado, María-Luisa ¹

Export

Export

×

MLA

modern-language-association

APA

apa

Chicago

chicago-fullnote-bibliography

Harvard

harvard-cite-them-right

Vancouver

vancouver-author-date

---

RIS BibTex

Export

×

This document cannot be exported because it belongs to another portal.

Full text

lock_openFull text externo lock_openFull text externo

DOI: 10.3390/BIOMIMETICS9120752 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Biomimetics

Abstract

Readmissions are an indicator of hospital care quality; a high readmission rate is associated with adverse outcomes. This leads to an increase in healthcare costs and quality of life for patients. Developing predictive models for hospital readmissions provides opportunities to select treatments and implement preventive measures. The aim of this study is to develop predictive models for the readmission risk of patients with schizophrenia, combining the particle swarm optimization (PSO) algorithm with machine learning classification algorithms. The database used in the study includes a total of 6089 readmission records of patients with schizophrenia. These records were collected from 11 public hospitals in Castilla and León, Spain, in the period 2005–2015. The results of the study show that the Random Forest algorithm combined with PSO achieved the best results across the evaluated performance metrics: AUC = 0.860, recall = 0.959, accuracy = 0.844, and F1-score = 0.907. The development of these new models contributes to -improving patient care. Additionally, they enable preventive measures to reduce costs in healthcare systems.