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Enhancing Employee Well-being with Machine Learning: Predictive Models for Health and Wellness Programs

Year: 2024

Type: article

Abstract: This research explores the use of machine learning (ML) to enhance employee well-being through predictive models designed to optimize health and wellness programs. Using the Random Forest algorithm, employee health data—such as activity levels, sleep patterns, and stress metrics—are analyzed to identify individuals at risk of health issues. By leveraging Python's Scikit-learn library, predictive insights are derived to recommend personalized wellness interventions. Results demonstrate that the model achieves high accuracy in predicting outcomes like burnout and health deterioration, enabling proactive support. The research highlights the potential of ML-driven wellness solutions in fostering healthier, more productive workplaces.

Source: Journal of Informatics Education and Research

DOI: 10.52783/jier.v4i3.1766

Author: Suchita Arora

Institution: Poornima University

Language: English

Topic: AI and HR Technologies

Subfield: Organizational Behavior and Human Resource Management

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Good health and well-being, Decent work and economic growth

Open Access status: diamond

OpenAlex ID: W4404765310

FWCI:

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Related to: 10