AI-Driven Behavioral Health Interventions Through E-commerce Purchase Patterns
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-111Keywords:
Behavioral Health, Artificial Intelligence, E-Commerce, Purchase Patterns, Machine Learning, Digital Phenotyping, Mental Health Intervention, Ethical AI, Consumer Behavior, Personalized HealthAbstract
As online shopping becomes an integral part of daily life, the data generated from e-commerce transactions offers valuable insights into consumer behavior and psychological states. This paper explores the use of artificial intelligence (AI) and machine learning techniques to analyze e-commerce purchase patterns as proxies for behavioral health indicators such as depression, anxiety, stress, or compulsive behaviors. By identifying these patterns, AI systems can offer real-time, personalized mental health interventions or referrals, bridging gaps in access to care while maintaining user privacy. The study proposes a framework for ethically integrating AI-driven behavioral assessments into e-commerce platforms, reviews existing literature on behavioral health informatics, and discusses the implications for public health, policy, and technology ethics
References
[1] Torous, J., & Nebeker, C. (2017). Navigating ethics in the digital age: introducing Connected and Open Research Ethics. JMIR Mental Health, 4(2), e20.
[2] Montag, C., & Elhai, J. D. (2020). Discussing digital technology overuse in children and adolescents during the COVID-19 pandemic and beyond: On the importance of considering affective neuroscience theory. Addictive Behaviors Reports, 12, 100313.
[3] Saeb, S., Zhang, M., Kwasny, M. J., & Mohr, D. C. (2015). Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: An exploratory study. Journal of Medical Internet Research, 17(7), e175.
[4] Huckvale, K., Torous, J., & Larsen, M. E. (2019). Assessment of the data sharing and privacy practices of smartphone apps for depression and smoking cessation. JAMA Network Open, 2(4), e192542-e192542.
[5] De Choudhury, M., Counts, S., & Horvitz, E. (2013). Predicting postpartum changes in emotion and behavior via social media. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 3267–3276.
[6] Schmidt, F. M., Sander, C., Dietz, M. E., Nowak, C., Schröder, T., Mergl, R., & Hegerl, U. (2017). Brain arousal regulation as potential biomarker for predicting antidepressant response in major depression. Scientific Reports, 7(1), 1-10.
[7] Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20.
[8] Choudhury, M. D., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13(1), 128-137.
[9] Orsolini, L., Papanti, D., Francesconi, G., & Schifano, F. (2015). Mindfulness-based therapy in the treatment of anxiety and depression. Journal of Addictive Diseases, 34(2-3), 177-183.
[10] Paredes, P., & Chan, M. (2011). CalmMeNow: exploratory research and design of stress mitigating mobile interventions. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2307–2316.
[11] Coppersmith, G., Leary, R., Crutchley, P., & Fine, A. (2018). Natural language processing of social media as screening for suicide risk. Biomedical Informatics Insights, 10, 1178222618792860.
[12] Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). ‘It’s reducing a human being to a percentage’: Perceptions of justice in algorithmic decisions. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14.
[13] Mehrotra, A., Musolesi, M., Hendley, R., & Pejovic, V. (2016). Designing content-driven intelligent notification mechanisms for mobile applications. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 813–824.
[14] Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. NPJ Digital Medicine, 3(1), 1-4.
[15] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
[16] Kirti Vasdev. (2019). “AI and Machine Learning in GIS for Predictive Spatial Analytics”. International Journal on Science and Technology, 10(1), 1–8. https://doi.org/10.5281/zenodo.14288363
[17] Puneet Aggarwal,Amit Aggarwal. "AI-Driven Supply Chain Optimization in ERP Systems Enhancing Demand Forecasting and Inventory Management", International Journal of Management, IT & Engineering, 13 (8), 107-124, 2023.
[18] Sahil Bucha, “Design And Implementation of An AI-Powered Shipping Tracking System For E-Commerce Platforms”, Journal of Critical Reviews, Vol 10, Issue 07, 2023, Pages. 588-596.
[19] Advanced Technique for Analysis of the Impact on Performance Impact on Low-Carbon Energy Systems by Plant Flexibility, Sree Lakshmi Vineetha Bitragunta1 , Lakshmi Sneha Bhuma2 , Gunnam Kushwanth3, International Journal for Multidisciplinary Research (IJFMR), Volume 2, Issue 6, November-December 2020,PP-1-9.
[20] Animesh Kumar, “Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML)”, Transactions on Engineering and Computing Sciences, 12(4), 59-69. 2024.
[21] Venu Madhav Aragani, 2025, “Implementing Blockchain for Advanced Supply Chain Data Sharing with Practical Byzantine Fault Tolerance (PBFT) Alogorithem of Innovative Sytem for sharing Suppaly chain Data”, IEEE 3rd International Conference On Advances In Computing, Communication and Materials.
[22] Swathi Chundru, Lakshmi Narasimha Raju Mudunuri, “Developing Sustainable Data Retention Policies: A Machine Learning Approach to Intelligent Data Lifecycle Management,” in Driving Business Success Through EcoFriendly Strategies, IGI Global, USA, pp. 93-114, 2025.
[23] Sudheer Panyaram, (2025). Optimizing Processes and Insights: The Role of AI Architecture in Corporate Data Management. IEEE.
[24] P. Pulivarthy Enhancing Data Integration in Oracle Databases: Leveraging Machine Learning for Automated Data Cleansing, Transformation, and Enrichment International Journal of Holistic Management Perspectives, 4 (4) (2023), pp. 1-18
[25] S. Bama, P. K. Maroju, S. Banala, S. Kumar Sehrawat, M. Kommineni and D. Kodi, "Development of Web Platform for Home Screening of Neurological Disorders Using Artificial Intelligence," 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), Bhimtal, Nainital, India, 2025, pp. 995-999, doi: 10.1109/CE2CT64011.2025.10939414.
[26] Marella, B. C. C., & Kodi, D. (2025). Fraud Resilience: Innovating Enterprise Models for Risk Mitigation. Journal of Information Systems Engineering and Management, 10, 683– 695. Scopus. https://doi.org/10.52783/jisem.v10i12s.1942
[27] Kodi, D. (2024). “Automating Software Engineering Workflows: Integrating Scripting and Coding in the Development Lifecycle “. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 635–652.
[28] Barigidad, S. (2025). Edge-Optimized Facial Emotion Recognition: A High-Performance Hybrid Mobilenetv2-Vit Model. International Journal of AI, BigData, Computational and Management Studies, 6(2), 1-10. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P101
[29] V. M. Aragani and P. K. Maroju, "Future of blue-green cities emerging trends and innovations in iCloud infrastructure," in Advances in Public Policy and Administration, pp. 223–244, IGI Global, USA, 2024.
[30] Naga Ramesh Palakurti Vivek Chowdary Attaluri,Muniraju Hullurappa,comRavikumar Batchu,Lakshmi Narasimha Raju Mudunuri,Gopichand Vemulapalli, 2025, “Identity Access Management for Network Devices: Enhancing Security in Modern IT Infrastructure”, 2nd IEEE International Conference on Data Science And Business Systems.
[31] Sudheer Panyaram, (2025), Artificial Intelligence in Software Testing, IGI Global, Sudheer Panyaram, (2024), Utilizing Quantum Computing to Enhance Artificial Intelligence in Healthcare for Predictive Analytics and Personalized Medicine, Transactions on Sustainable Computing Systems, 2(1), 22-31, https://www.fmdbpub.com/user/journals/article_details/FTSCS/208
[32] Padmaja Pulivarthy, (2024/3/9). Semiconductor Industry Innovations: Database Management in the Era of Wafer Manufacturing. FMDB Transactions on Sustainable Intelligent Networks. 1(1). 15-26. FMDB.
[33] Swathi Chundru, Siva Subrahmanyam Balantrapu, Praveen Kumar Maroju, Naved Alam, Pushan Kumar Dutta, Pawan Whig, (2024/12/1), AGSQTL: adaptive green space quality transfer learning for urban environmental monitoring, 8th IET Smart Cities Symposium (SCS 2024), 2024, 551-556, IET.
[34] Kommineni, M. "Explore Knowledge Representation, Reasoning, and Planning Techniques for Building Robust and Efficient Intelligent Systems." International Journal of Inventions in Engineering & Science Technology 7.2 (2021): 105- 114.
[35] Mohanarajesh Kommineni, (2023/9/17), Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware, International Journal of Innovations in Applied Sciences & Engineering, 9. 48-59. IJIASE. - 1
