While there are several works that diagnose acute stress using electroencephalographic recordings and machine learning, there are hardly any works that deal with chronic stress. Currently, chronic stress is mainly determined using questionnaires, which are, however, subjective in nature. While chronic stress has negative influences on health, it also greatly influences decision-making processes in humans. In this paper we propose a novel machine learning approach based on the fine-graded

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... l analysis of resting-state EEG recordings, to diagnose chronic stress. By using this new machine learning approach, we achieve a very good balanced accuracy of 81.33%, outperforming the current benchmark by 10%. Our algorithm allows an objective assessment of chronic stress, is accurate, robust, fast and cost-efficient and substantially contributes to decision-making research, as well as Information Systems research in healthcare.