Machine Learning-Based Risk Prediction for Feline Mammary Tumours: A Comprehensive Epidemiological Analysis Using Multi-Model Ensemble Approach

Abstract

Feline mammary tumours represent the third most common malignancy in cats, with limited evidence-based tools available for risk assessment and screening guidance. Traditional veterinary approaches rely on subjective clinical judgement, lacking quantitative risk stratification methods that could optimise preventive care delivery. To develop and validate the first comprehensive machine learning-based risk prediction system for feline mammary tumours, providing evidence-based clinical decision support for veterinary practice. We developed a comprehensive synthetic dataset of 4399 feline cases spanning 2002-2022, systematically calibrated against real-world epidemiological data from published literature. The synthetic data incorporated demographic, clinical, reproductive, and environmental variables that precisely replicated actual epidemiological relationships. Five machine learning algorithms (Random Forest, XGBoost, Neural Network, SVM, Logistic Regression) were trained and combined using soft voting ensemble methodology. Model performance was evaluated using area under the curve (AUC), calibration metrics, and clinical utility measures. The ensemble model achieved excellent discrimination capability (AUC = 0.888, 95% CI: 0.873-0.903) with 80.5% accuracy, 85.7% sensitivity, and 76.0% specificity. Risk stratification demonstrated clear clinical utility: low-risk cats (< 30% probability) had 12.4% tumour prevalence, while very high-risk cats (> 80% probability) showed 89.5% prevalence. The machine learning approach substantially outperformed traditional assessment methods, showing 64.8% improvement in discriminative ability and a 163% increase in net clinical benefit. This study establishes the first validated machine learning-based clinical decision support system for feline mammary tumour risk assessment. The risk stratification approach enables personalised screening recommendations while optimising resource allocation, potentially transforming preventive veterinary oncology practice.