Predictive Analytics for Retention in Care and Antiretroviral Therapy Adherence using Supervised Learning – A Case Study of County Health Facilities in Kenya (2023)
Geoffrey Sagwe, Andrew M. Kahonge,Teresa K. Mwendwa JCR. 2023: 397-404
In healthcare organizations, a great problem is faced by healthcare providers to know the ART adherence and status of HIV/AIDS patients. In this research, a predictive model using supervised learning is developed to let clinicians and healthcare providers know the ART adherence of PLHIV using features of the patients’ treatment history. The methodology used was CRISP-DM data mining process. The research used easily measurable baseline demographic and clinical variables such as body weight, ART regimen, patients enrolled in care, and phenotype. Data preprocessing and transformation was done to ensure the dataset collected was clean. The dataset was split into training and test set i.e., 80% for training and 20% for testing. The baseline results from the benchmark and performance evaluation showed that random forest model performed the best with accuracy of 81% and AUC of 79.3% compared to other binary algorithms and classification error rate of 0.333. The model that performed poorly was Naïve Bayes with an accuracy score of 20.0%. The model that performed poorly was Naïve Bayes with an accuracy score of 20.0%. The researcher retrospectively followed 21551 records of patients who were seeking care at county health facilities.
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the Budapest Open Access Initiative (BOAI) definition of open access.
The articles in Journal of Critical Reviews are open access articles licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc-sa/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Copyright � 2021 Journal of Critical Reviews All Rights Reserved. Subject to change without notice from or liability to Journal of Critical Reviews.
For best results, please use Internet Explorer or Google Chrome
Journal of Critical Review, Tower 23/4,
Kuala Lumpur, malaysia