Leveraging Machine Learning for Evaluating Homeopathic Treatment Efficacy in Canine Parvovirus: A Retrospective Analysis of 118 Cases

Abstract

Canine parvovirus (CPV) is a highly virulent infection affecting young dogs, posing a major clinical challenge due to rapid disease progression, high mortality up to 90% in untreated cases, and resistance to conventional supportive treatments. Homeopathy, employing an individualized symptom-based selection of remedies, offers a complementary therapeutic modality. According to Organon §153, rare, peculiar, and characteristic symptoms guide remedy selection most precisely. This study employs advanced Machine Learning (ML) techniques to analyze a dataset of 118 CPV-positive canine cases treated with homeopathic remedies, aiming to validate treatment efficacy and derive actionable clinical insights. The dataset includes key clinical variables such as disease severity, symptom presentation, laboratory markers, and treatment outcomes. Statistical and ML-based predictive modelling (Logistic Regression, Random Forest, XG boost, and Correlation analysis) identified disease severity as the most significant predictor of survival (χ², p < 0.001), with all mild cases recovering and severe cases exhibiting a 64% survival rate. ML-driven feature importance analysis confirmed that individualized homeopathic remedy selection, guided by symptom clustering, was crucial to treatment success. Phosphorus, Arsenicum album, and Cadmium sulphuratum were the primary remedies, each corresponding to distinct symptom profiles. The overall survival rate of 89%—comparable to outcomes from conventional intensive care—suggests that homeopathic treatment, when systematically applied, can be effective in CPV management. By leveraging ML for pattern recognition and predictive modeling, this study provides empirical support for homeopathic intervention while enhancing clinical decision-making precision, predicting outcomes reliably, streamlining individualized remedy selection, and possibly improving survival rates even further. This leveraging logically complements existing homeopathic methodology by strengthening symptom-remedy matching, prognostic accuracy, and documentation quality. Future research integrating ML with real-time clinical data can further refine predictive models, optimize homeopathic protocols, and establish more robust evidence for its significant benefits for animal welfare and practitioner efficiency.