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- Title
Integrating AI and ML for Enhanced Homeopathic Management of Canine Epilepsy.
- Authors
Makker, Surjit Singh; Yadav, Tarachand
- Abstract
Canine epilepsy, a prevalent neurological disorder, poses significant challenges in veterinary medicine. Traditional treatment approaches often yield variable outcomes, necessitating the exploration of alternative therapies. This study investigates the efficacy of homeopathic remedies in managing canine epilepsy and explores the potential of Artificial Intelligence (AI) and Machine Learning (ML) to enhance treatment protocols while preserving the individualized nature of homeopathy. Data were collected from 32 epileptic dogs, including various parameters such as age, breed, sex, weight, seizure frequency and duration, pre-and post-treatment, homeopathic remedies used, potency, treatment duration, and owner observations. The remedies administered included Agaricus mus, Belladonna, Cicuta virosa, and Natrum mur., with potencies ranging from 30C to 1M and in LM potency. Treatment durations varied from 2 to 24 months. AI/ML models were designed to analyze patient-specific data, considering each dog's unique symptom profiles and characteristics to ensure that homeopathy's individualized nature was preserved. The models focused on identifying patterns that complement, rather than replace, the practitioner's symptoms analysis. Three ML algorithms--logistic regression, random forest, and XGBoost-- were trained to analyze the data and predict treatment outcomes, with the random forest model emerging as the most accurate. The analysis revealed that individualized homeopathic treatments, particularly those with prolonged administration and specific remedies like Natrum mur in LM potencies, showed significant efficacy in managing epilepsy. The AI/ML models identified key predictors of treatment success, such as pre-treatment seizure frequency, remedy used, and treatment duration, while ensuring that these predictions aligned with the unique symptoms of each dog. The random forest model demonstrated an accuracy of 86% in predicting treatment success. Notably, the AI/ML outputs were presented in a format that was interpretable by practitioners, such as predicted success rates and feature importance scores, which provided actionable insights to guide treatment decisions without undermining the individualized approach of homeopathy. This study demonstrates that integrating AI and ML into homeopathic practice can enhance the management of canine epilepsy by providing robust, data-driven tools that assist practitioners in making informed decisions. The AI/ML models were designed to support, not replace, the practitioner's judgment, ensuring that the core principles of homeopathy, particularly unicism are maintained. The final selection of homeopathic remedies remains the practitioner's responsibility, with AI/ML as a tool to augment clinical decisionmaking. Future research will focus on refining these technologies to ensure their ethical and effective use in homeopathic practice, ultimately improving the quality of life for affected dogs.
- Subjects
MACHINE learning; HOMEOPATHIC agents; ARTIFICIAL intelligence; RANDOM forest algorithms; NATURE reserves
- Publication
International Journal of High Dilution Resarch, 2024, Vol 24, Issue 1, p46
- ISSN
1982-6206
- Publication type
Academic Journal
- DOI
10.51910/ijhdr.v24i1.1503