An Enhanced Affective Computing Technology for Fostering an EmotionallyHealthy Workplace

The mental health of workers is of paramount importance in today’s fast-paced and demanding workplaces. This research establishes a strong connection between affective decision-making and mental health, presenting a novel method to improve affective computing technologies for developing emotionally healthy workplaces. We use Multinomial Naive Bayes Integrated Gated Recurrent Units (MNB-GRU) for classification and prediction to reach our goals. This dataset includes many measures of mental health, working climate, and individual variables. Data Exploration is performed to learn more about the properties of the dataset, and Preprocessing Grouping is used to get the data ready for analysis. Relationships between emotional decisionmaking and mental health markers are shown using data visualization approaches to give intuitive insights. To evaluate the reliability of the connection, a correlation analysis is used in Model Assessment. Understanding how people are feeling emotionally at work can be gained via this assessment, which examines the degree of association between affective decision-making and mental health. As a result of using categorization methods to divide the workforce into several categories based on their emotional well-being, we can better assist businesses in meeting the varying demands of their staff members. This method guarantees that efforts to improve mental health are focused and productive. By creating a solid connection between effective decisionmaking and mental health, businesses can take preventative measures to aid their workers' emotional well-being and create a more upbeat and productive workplace.