https://rajpub.com/index.php/ijmit KHALSA PUBLICATIONS en-US INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 2278-5612 <p><a href="http://creativecommons.org/licenses/by/4.0/" rel="license"><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" alt="Creative Commons License" /></a> All articles published in <em>Journal of Advances in Linguistics</em> are licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> https://rajpub.com/index.php/ijmit/article/view/9844 <p><span style="font-weight: 400;">It is important to understand what might cause healthcare professionals to leave their jobs. In this research, we therefore analyze data on employee attrition in the healthcare sector to determine which factors motivate these professionals to leave or stay in their current careers. We combine the flexibility of machine learning techniques with the transparency of traditional statistical techniques, such as logistic regression analysis, to understand the data. With an accuracy rate of 95.6%, based on several factors in this study, we find that one of the primary reasons these healthcare professionals leave their jobs is excessive overtime requirements. Using a deeper analysis involving logistic regression, we determine the quantitative effects of our different explanatory variables. We also find that job satisfaction does not seem to have as much explanatory power as several other variables, and that it does not seem to be a mediating variable in explaining attrition.</span></p> Sumali Conlon Yue Gaob Haitao Liuc Copyright (c) 2026 Sumali Conlon, Yue Gaob, Haitao Liuc https://creativecommons.org/licenses/by/4.0 2026-01-17 2026-01-17 21 1 15 10.24297/ijmit.v21i.9844