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Multi-Label Machine Learning Framework for Homoeopathic Remedy Recommendation using Clinical Disease Descriptions
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 13 - Number 5 |
| Year of Publication: 2026 |
| Authors: Rincy T.A. |
10.5120/ijais7debbbd4f6e2
|
Rincy T.A. . Multi-Label Machine Learning Framework for Homoeopathic Remedy Recommendation using Clinical Disease Descriptions. International Journal of Applied Information Systems. 13, 5 ( Oct 2026), 1-5. DOI=10.5120/ijais7debbbd4f6e2
Abstract
The application of artificial intelligence in healthcare has created new opportunities for developing data-driven clinical decision-support systems. This study explores the use of machine learning for recommending multiple Homoeopathic remedies based on clinical disease descriptions. A multi-label classification framework was developed using text preprocessing and TF-IDF-based feature representation. Different machine learning algorithms were comparatively evaluated to identify their effectiveness in predicting associated remedies. The findings demonstrate the potential of machine learning techniques for automated remedy recommendation, while also highlighting challenges related to data sparsity, class imbalance, and the diversity of remedy associations. The proposed approach provides a foundation for developing intelligent tools that can support practitioners in analysing clinical descriptions and identifying potential remedy options.
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