PAW Healer: Towards an Intelligent and Autonomous Pet Health Management System

Abstract

Pets are vital to human lives, and their wellbeing depends on prompt medical attention. However, early disease detection and effective management of vaccination schedules and veterinary consultations are frequently challenges for pet owners. This paper introduces Paw Healer, an AI-driven pet care support system that uses pet owners’ symptoms to forecast pet illnesses. In order to categorize disease severity into mild, moderate, and severe groups, machine learning models are utilized to examine symptom patterns. The system offers advice on grooming and at- home care or, if required, suggests veterinary consultation based on the prediction results. In order to avoid missed immunizations, the system also creates timely reminders and keeps digital vaccination records. Additionally, in order to detect possible disease outbreaks, area-based disease forecasting is carried out by examining symptom patterns in various locations. All pet health information is safely kept on a cloud platform, and both pet owners and veterinarians can access it via an easy-to-use web interface. The suggested system facilitates informed veterinary care decision-making, enhances early disease detection, and improves pet health management.

Introduction

The text discusses the growing need for accessible and efficient pet healthcare due to increasing pet ownership and the limitations of traditional methods, which rely on manual record-keeping and lack early disease detection. Many pet owners cannot recognize symptoms in time, leading to delayed treatment and serious health issues. Although AI and machine learning have improved healthcare, existing pet care apps mostly offer basic features and lack advanced capabilities like disease prediction and outbreak detection.

To address these gaps, the paper proposes Paw Healer, an AI-powered pet healthcare system that integrates disease prediction, severity classification, preventive care suggestions, and regional outbreak forecasting. Users can input pet symptoms through a web or mobile interface, and the system analyzes them using machine learning models to suggest possible illnesses and recommend care or veterinary consultation. It also maintains digital health records and sends vaccination reminders.

The system architecture includes three main modules: a user and pet management module for profiles and symptom input, a data processing module for analyzing trends and forecasting outbreaks, and a machine learning/admin module for model training and medical validation. It uses cloud integration and forecasting techniques like time-series analysis to predict regional disease trends.

The methodology involves a role-based system design with mobile and web interfaces, along with data collection and preprocessing to train accurate prediction models.

Overall, the proposed system aims to improve early diagnosis, enable preventive care, reduce treatment delays, and support better decision-making for both pet owners and veterinarians through an integrated AI-driven platform.

Conclusion

In order to meet the increasing demand for intelligent, easily accessible, and preventive pet healthcare solutions, this work detailed the design and development of an AI-based Pet Care Assistance System. To assist both pet owners and veterinary professionals, the suggested solution combines machine learning-based disease prediction with severity classification, home care advice, and veterinary service administration. Veterinarians and administrators may effectively manage clinical data through web-based portals, while pet owners can easily input symptoms and receive healthcare services through mobile applications. For the purpose of classifying diseases, the system uses Logistic Regression, which offers a clear and computationally effective baseline model. Furthermore, the basis for predicting future illness patterns is established by time-series analysis of confirmed disease cases. The system’s overall usefulness and practicality are improved by supporting features including appointment scheduling, vaccine tracking, cloud-based data storage, and user-friendly interfaces. The outcomes of the trial show that the suggested strategy can successfully support early disease detection and well-informed decision-making. All things considered, the suggested AI- driven framework shows great promise for enhancing preventive pet healthcare and lessening the effects of delayed diagno- sis. The system provides a scalable and expandable platform for intelligent veterinary support by fusing real-time service integration with predictive analytics. Future improvements are made possible by the modular architecture, which makes the solution appropriate for practical implementation and ongoing research in AI-based animal healthcare systems.

References

[1] A. I. Pereira, Pedro Leite, ”Artificial Intelligence in Veterinary Imaging: An Overview,” Vet. Sci., vol. 10, no. 5, p. 320, April 2023, doi: 10.3390/vetsci10050320 [2] A. Dourson, Robert Santilli, ”PulseNet: Deep Learning ecg-signal classification using random augmentation policy and continous wavelet transform for canines,” arXiv preprint arXiv:2305.15424, May 2023, doi: 10.48550/arXiv.2305.15424 [3] Jooho Lee,V T Hoang, ”Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning,” in 2023 IEEE/ACM 24th International Conference on Computer Sup- ported Cooperative Work and Social Computing (CSCW), December 2023, doi: 10.1109/ACCESS.2023.3324046. [4] Ibrahim Idris ,Sammuel S, ”The potential application of artifi- cial intelligence in veterinary clinical practice and biomedical re- search,” J. Vet. Sci., vol. 22, no. 1, p. e17, January 2024, doi: 10.3389/fvets.2024.1347550. [5] Silvia Burti, Simon Coghlan,”Artificial intelligence in veterinary diag- nostic imaging: Perspectives and limitations,” Vet. Sci., vol. 10, no. 5, p. 320,May 2024, doi: 10.3390/vetsci10050320. [6] Namrah zaman, H.S Won, ”PETIS: Intent Classification and Slot Filling for Pet Care Services,” ACM Trans. Asian Lang. Inf. Process., vol. 22, no. 3, p. 1-19, August 2024, doi:10.1109/ACCESS.2024.3452771 [7] R. L. Pinho, Jonathan Williams, ”The digital revolution in veterinary pathology,” J. Vet. Diagn. Invest., vol. 34, no. 5, p. 774-781, August 2024, doi: 10.1016/j.jcpa.2024.08.001 [8] M. Zuffi, M.Gauly , ”Short communication: Do veterinary diagnoses coming from electronic recording system of veterinary treatments have the potential to be used for breeding in small populations? The case of the dual-purpose Alpine Grey cattle breed,” Animal, vol. 17, no. 1, p. 100723, September 2024, doi: 10.1016/j.animal.2024.101351. [9] Joonho Lim, Daehyun Pak, ”Advancing Pet Biometric Identification: A State-of-the-Art Unified Framework for Dogs and Cats,” in 2023 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), October 2024, doi: 10.1109/ACCESS.2024.3452771. [10] B. Cetintav, ”Generative AI Meets Animal Welfare: Evaluating GPT-4 for Pet Emotion Detection,” Animals, vol. 15, no. 4, p. 492, December 2024, doi: 10.3390/ani15040492

Copyright

Copyright © 2026 Mubashir K C, Nandana K P, Neha Ramesh, Shahanas C P, Ms. Vismaya Vijayan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.