Most pet owners genuinely care about their animals, but in reality, very few maintain proper digital health records. Vaccination dates, weight history, and previous medical issues are often stored in paper form or simply forgotten. Small signs like reduced appetite or unusual tiredness are sometimes ignored until the condition becomes serious. To address this issue, a desktop-based Smart Animal Health Monitoring and Diagnosis System was developed. The application is built using Python, with PyQt5 for the graphical interface and SQLite for local database storage. It allows users to store pet details, track growth using graphical visualization, set reminders for vaccination or medication, analyze symptoms using a rule-based scoring system, and view nearby veterinary locations. The system does not use complex medical datasets but instead applies a simple and interpretable scoring logic to classify health conditions into Low, Moderate, or High risk categories. The main objective of this project is to assist pet owners in early health awareness and organized record management rather than replacing professional veterinary advice.
Smart Animal Health System, Decision Support System, Rule-Based Scoring Algorithm, Graphical User Interface (GUI), Local Database Management, Health Risk Classification, Modular Application Architecture, Preventive Healthcare Technology, Human–Computer Interaction, AI-Assisted Pet Care
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