AI Based Breath Volatile Organic Compounds Pattern Recognition System for Early Health Risk Screening

Harish S; Jackson S; Gunasundari J

doi:10.65379/tpsn2013/ijaemsv02i01p1

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AI Based Breath Volatile Organic Compounds Pattern Recognition System for Early Health Risk Screening

Author(s): Harish S, Jackson S, Gunasundari J

Volume RegistryVolume 2

Issue PeriodIssue 01

Published Date08 Jan 2026

DOI Identifier🔗 10.65379/tpsn2013/ijaemsv02i01p1

Abstract

Volatile Organic Compounds (VOCs) in human breath contain important biochemical data that can be used as early warning signs for health risk awareness systems. The proposed research work brings forth an AI pattern that utilizes VOC patterns to ensure health screening is noninvasive, rapid, and inexpensive. The proposed system combines health data acquisition through sensor technology and sophisticated machine learning algorithms to detect minute changes in VOC patterns and convert them into predictive risk profiles. A prototype system has been developed to test real-time breath monitoring through IoT technology and a mobile feasibility interface, proving the feasibility of portable health screening outcomes. Experimental methodologies have been used to ensure the system is highly sensitive and specific in detecting minute health anomalies while remaining cost-effective. This study brings together the ideas of artificial intelligence and breath analysis to help in the development of proactive and preventive healthcare technology that is less invasive in terms of health screening. The proposed system fills the gap that exists between the laboratory-based diagnostic systems and the health resources available in the community that are more accessible. The system that involves sensor integration and AI recognition is one of the approaches through which portable technology and the power of breath are able to provide valuable chemistry. The prototype system indicates the significance of affordability and flexibility in ensuring that the system is relevant in the context of healthcare monitoring. The smartphone interface enhances the usability of the system and encourages proactive personal health.

Keywords

Artificial intelligence Breath analysis Volatile organic compounds Patterns recognition screening IoT-enabled Early health risk monitoring Real-time data processing.

References (15)

Vadivel, Manonmani, et al. "A Comprehensive Review on Breath Analyzer as a Point-of-Care Device for VOC-Based Lung Cancer Risk Prediction using Deep Learning." International Conference on Intelligent Information Systems Design and Indian Knowledge System A pplications (ICISDIKSA 2026). Atlantis Press, 2025,Doi:https://doi.org/10.2991/978-94-6463- 976-6_8
Vinhas, M., Leitão, P. M., Raimundo, B. S., Gil, N., Vaz, P. D., & Luis-Ferreira, F. (2023). AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer. Cancers, 16(12), 2200. https://doi.org/10.3390/cancers16122200
Haripriya, P., Madhavan Rangarajan, and Hardik J. Pandya. "Breath VOC analysis and machine learning approaches for disease screening: a review." Journal of Breath Research 17.2 (2023): 024001,DOI https://doi.org/10.1088/1752-7163/acb283
Jalal, Ahmed H., et al. "Prospects and challenges of volatile organic compound sensors in human healthcare." Acs Sensors 3.7 (2018): 1246-1263, doi: https://doi.org/10.1021/acssensors.8b00400
Zhu, Jianxiong, Zhihao Ren, and Chengkuo Lee. "Toward healthcare diagnoses by machine-learning-enabled volatile organic compound identification." ACS nano 15.1 (2020): 894-903, Doi:https://doi.org/10.1021/acsnano.0c07464
S. Nivithaa, A. Mondal and A. Tripathy, "Early Diagnosis of Lung Cancer Through VOC Detection Using an Affine Deep Learning E-Nose System," in IEEE Access, vol. 13, pp. 181429- 181442, 2025, doi: https://doi.org/10.1109/ACCESS.2025.3619104
D. Khanna, A. Kumar and S. Ahmad Bhat, "Volatile Organic Compounds for the Prediction of Lung Cancer by Using Ensembled Machine Learning Model and Feature Selection," in IEEE Access, vol. 13, pp. 9809-9820, 2025, doi: https://doi.org/10.1109/ACCESS.2025.3527027
Chen, M., Zhang, M., Yang, Z., Zhou, C., Cui, D., Haick, H., & Tang, N. (2023). AI-Driven Wearable Mask-Inspired Self-Healing Sensor Array for Detection and Identification of Volatile Organic Compounds. Advanced Functional Materials, 34(3), 2309732. https://doi.org/10.1002/adfm.202309732
Yang, Z., Liu, Y., Chen, D., Miao, J., Chen, M., Liu, G., Gao, G., Guo, Y., Cui, D., & Li, Q. (2024). A battery-free, wireless, flexible bandlike e-nose based on MEMS gas sensors for precisely volatile organic compounds detection. Nano Energy, 127, 109711. https://doi.org/10.1016/j.nanoen.2024.109711 E-ISSN: 3107-5843 International Journal of Advanced Engineering and Management System Volume 02, Issue 01, January-March 2026, pp. 11-24
M. Abouhawwash and A. M. Alessio, “Multi-objective evolutionary algorithm for pet image reconstruction: Concept,” IEEE Transactions on Medical Imaging, vol. 40, no. 8, pp. 2142–2151, 2021.
P. Sukul, J. K. Schubert, K. Zanaty, P. Trefz, A. Sinha et al., “Exhaled breath compositions under varying respiratory rhythms reflects ventilatory variations: Translating breathomics towards respiratory medicine,” Scientific Reports, vol. 10, no. 1, pp. 1–16, 2020.
A. R. Javed, M. U. Sarwar, M. O. Beg, M. Asim, T. Baker et al., “A collaborative healthcare framework for shared healthcare plan with ambient intelligence,” Human-Centric Computing and Information Sciences, vol. 10, no. 1, pp. 1–21, 2020
T. Richter, B. Fishbain, A. Markus, G. Richter-Levin and H. Okon-Singer, “Using machine learning-based analysis for behavioral differentiation between anxiety and depression,” Scientific Reports, vol. 10, no. 1, pp. 1–12, 2020.
M. Rizwan, A. Shabbir, A. R. Javed, G. Srivastava, T. R. Gadekallu et al., “Risk monitoring strategy for confidentiality of healthcare information,” Computers and Electrical Engineering, vol. 100, no. 1, pp. 107833, 2022.
Kokkotis, C., Moustakidis, S., Swift, S. J., Kontopidou, F., Kavouras, I., Doulamis, A., & Giannoukos, S. (2025). Artificial Intelligence and Machine Learning in the Diagnosis and Prognosis of Diseases Through Breath Analysis: A Scoping Review. Information, 16(11), 968. https://doi.org/10.3390/info16110968

Harish S, Jackson S, and Gunasundari J, "AI Based Breath Volatile Organic Compounds Pattern Recognition System for Early Health Risk Screening," Int. J. Adv. Eng. Manag. Syst., vol. 2, no. 1, pp. 225-238, 2026. doi: 10.65379/tpsn2013/ijaemsv02i01p1.

AI Based Breath Volatile Organic Compounds Pattern Recognition System for Early Health Risk Screening

Abstract

Keywords

References (15)

Format Citation Record