Predictive Model Based on Machine Learning to Decrease Patient Attrition in Health Care Institutions in Lima Using Python

Autores/as

  • Christian Ovalle Paulino Universidad Tecnologica del Peru, Peru

DOI:

https://doi.org/10.18687/LACCEI2024.1.1.248

Palabras clave:

Predictive Model, Machine Learning, Python, Logistic Regression.

Resumen

The present research addresses one of the main problems that prevent health problems in the country from being combated, identifying it as a significant challenge in health management. For this reason, it is necessary to generate a detection and/or prevention tool for these cases, so a predictive model is proposed to anticipate patients prone to drop out of the services of a health center. The research focuses on the Sanna El Golf clinic, where, by means of a predictive analysis, a 67% of assertiveness is obtained as a result, this approach shows substantial benefits for the clinic and highlights its contribution to meet the objectives set. In addition, the proposed model is positioned as a key tool in the prevention of medical attrition, identifying it as a significant challenge in health management.

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Publicado

2024-07-27

Número

Sección

Articles

Licencia

Licencia Creative Commons

Esta obra está bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.

LACCEI conserva el copyright de todos los artículos publicados bajo los términos de su acuerdo de transferencia de copyright. Como titular del copyright, LACCEI distribuye los artículos al público bajo la Licencia Internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0 (CC BY-NC-SA 4.0).

Cómo citar

Ovalle Paulino, C. (2024). Predictive Model Based on Machine Learning to Decrease Patient Attrition in Health Care Institutions in Lima Using Python. LACCEI, 1(10). https://doi.org/10.18687/LACCEI2024.1.1.248