Intelligent algorithms applied to stress profile analysis with a gender perspective
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Abstract
Stress responses and coping styles vary among individuals. Under deemed risky situations elicting behavioral, physiological, and psychological responses, a gender approach is crucial for setting up realistic measures in promoting the worker ́s health. Currently, from a psychological and psychobiological perspective, scales are applied to measure stress levels and coping styles among individuals.
This study was conducted to obtain an indicator or model that allows displaying existing vvulnerability levels among risk levels, analyzing it from a gender perspective, comparing women and men. The selected indicator is the level of stress, estimated by applying the Nowack Stress Profile instrument. The results of three studies conducted in different contexts were compiled and analyzed from a gender perspective. The first study took place in a laboratory, and scales were administered to detect depression and stress (n = 136 students). The second study examined the relationship between sleep and cognitive decline (n = 14 older adults). The third study was conducted on workers at the National Institute of Statistics and Geography (n = 116), with the purpose of promoting mental health. The analysis of data obtained from the instrument was carried out using intelligent machine learning algorithms, specifically k-means, Decision Tree (DT), and Random Forest (RF), with different configurations of the datasets from the mentioned studies. With the gender perspective analysis, the best model was obtained from the RF algorithm and the first four relevant dimensions were psychological well-being, social support network, health habits, and negative appraisal. Additionally, in coping with stress, the percentage of women with fewer resources was lower than that of men. These analyses allow focus on the group with higher level of risk in order to generate early prevention and stress management strategies.
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