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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Open Access Research Article

Predictive analytics for patient adherence : A machine learning approach in medication management

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pp. 249–260Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1251XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1251
Pages:
249–260

Abstract

In this paper, we investigate the use of predictive analytics and machine learning to improve patients’ adherence to prescribed medications. By analyzing past patient data, we are able to create a model that may foresee adherence behaviors based on characteristics like age, gender, health status, and income. In order to analyze and identify major drivers of non-adherence and enable personalized therapies, our method incorporates cutting-edge machine learning techniques. The methodology takes into account each individual’s situation while making suggestions, leading to a more proactive and specific strategy for improving drug adherence. Predictive analytics allows healthcare providers to foresee adherence issues, intervene at the right time, and boost patient outcomes. This research contributes to the developing landscape of healthcare by utilizing the potential of machine learning for precision medicine in drug management, addressing a vital part of patient care and public health

Keywords

Subject Classifications

68Q32

References

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