Determinants of anxiety levels among young males in a threat of experiencing military conflict–Applying a machine-learning algorithm in a psychosociological study

BACKGROUND: Anxiety could be felt even in objectively peaceful situations, but a vision of conflict could result in increased stress levels. In this article, we aimed to identify hidden patterns of mental conditions and create male profiles to illustrate the different subgroups as well as determinants of anxiety levels among them in accordance with proximity to a possibility of direct exposure to military action. METHODS: A sample of Ukrainian males, in duty as conscripts to military service (n = 392, M±SD = 22.1±5.3) participated in a survey. We used the 36-Item Short Form Health Survey, and State-Trait Anxiety Inventory. In addition to psychological indices, social-demographic data were collected. To discover the number of clusters, the k-means algorithm was used, the optimal number of clusters was found by the elbow algorithm. For validation of the model and its use for further prediction, the random forest machine-learning algorithm, was used. RESULTS: By performing k-means cluster analyses, 3 subgroups were identified. High values of psychological indices dominated in Subgroup 2, while lowest values dominated in Subgroup 3. Subgroup 1 showed a more even distribution among the indices. The strength of the relevance and main determinants of the prediction of the presented model mostly consisted of mental qualities, while socio-demographic data were slightly significant. CONCLUSIONS: There is no clear relevance between proximity or even the experience of military actions and anxiety levels. Other factors, mostly subjective feelings about mental conditions, are crucial determinants of feeling anxiety.