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Peer-reviewedCC BY-NC 4.0Technology and Health Care

Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior

  • Eleonora Milić1
  • Bratislav Predić1
  • Suzana Tošić Golubović2
  • Milica Cvetanović3
  1. 1Computer Science Department, Faculty of Electronic Engineering, University of Niš, Niš, Serbia
  2. 2Psychiatry department, Faculty of Medicine, University of Niš, Niš, Serbia
  3. 3Clinic for Anesthesia and Intensive Therapy of University Clinical Center Niš, Niš, Serbia
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Abstract

Background: In the digital era, iGaming has become a rapidly expanding phenomenon that generates complex behavioral patterns and increases the risk of problematic gambling. The integration of technologies such as Artificial Intelligence of Things and the Internet of Behavior enables a shift from reactive to preventive approaches in public health protection.

Objective: This study combines clinical practice, AI, machine learning and the Internet of Behavior concept to analyze player behavior patterns and develop machine learning models for early detection of addiction risk through behavioral markers.

Methods: Behavioral data were collected from an online game supplier operating through 52 operators in Republika Srpska, Croatia, Romania, Brazil, Somalia, and Mali. Psychiatric expertise and clinical experience were applied to identify harmful behavioral markers, which served as inputs for training MLP neural networks. Models trained per country classified player behavior into recreational, risky, and problematic categories.

Results: The analysis included 109,418 players across three continents, aggregating 5,135,179,510 online slot game bets. Results revealed significant cross-country variation in risky and problematic gambling, shaped by socio-economic, cultural, and regulatory factors.

Conclusion: The integration of AI-driven behavioral analysis with psychiatric insight provides a robust framework for early risk detection and personalized interventions supporting responsible gaming.

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Cite

BibTeXRIS
BibTeX
@article{milic2026behavioral,
  title = {{Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior}},
  author = {Eleonora Milić and Bratislav Predić and Suzana Tošić Golubović and Milica Cvetanović},
  journal = {Technology and Health Care},
  year = {2026},
  month = jun,
  doi = {10.1177/09287329261458197},
  url = {https://doi.org/10.1177/09287329261458197},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Behavioral patterns in iGaming across territories: Psychiatric and AI-driven insights via the internet of behavior
AU  - Eleonora Milić
AU  - Bratislav Predić
AU  - Suzana Tošić Golubović
AU  - Milica Cvetanović
PY  - 2026
DA  - 2026/06/24
JO  - Technology and Health Care
DO  - 10.1177/09287329261458197
UR  - https://doi.org/10.1177/09287329261458197
AB  - Background: In the digital era, iGaming has become a rapidly expanding phenomenon that generates complex behavioral patterns and increases the risk of problematic gambling. The integration of technologies such as Artificial Intelligence of Things and the Internet of Behavior enables a shift from reactive to preventive approaches in public health protection.  Objective: This study combines clinical practice, AI, machine learning and the Internet of Behavior concept to analyze player behavior patterns and develop machine learning models for early detection of addiction risk through behavioral markers.  Methods: Behavioral data were collected from an online game supplier operating through 52 operators in Republika Srpska, Croatia, Romania, Brazil, Somalia, and Mali. Psychiatric expertise and clinical experience were applied to identify harmful behavioral markers, which served as inputs for training MLP neural networks. Models trained per country classified player behavior into recreational, risky, and problematic categories.  Results: The analysis included 109,418 players across three continents, aggregating 5,135,179,510 online slot game bets. Results revealed significant cross-country variation in risky and problematic gambling, shaped by socio-economic, cultural, and regulatory factors.  Conclusion: The integration of AI-driven behavioral analysis with psychiatric insight provides a robust framework for early risk detection and personalized interventions supporting responsible gaming.
SN  - 0928-7329
SN  - 1878-7401
ER  -