Traditional Chinese medicine disease classification based on sparrow search algorithm
Abstract
This study aims to explore the application of various basic machine learning algorithms in the task of TCM (Traditional Chinese Medicine) disease classification, and select the best performing model for optimization through comparative analysis. After experimental verification, the random forest model has excellent performance in various evaluation indexes, and its accuracy and recall are 65.1%, 65.1%, respectively, showing its comprehensive performance advantage in the classification of TCM disease types. In order to further improve the performance of the model, the sparrow search algorithm was introduced to optimize the random forest model. The performance of the optimized model on the test set is significantly improved, with an accuracy of 74.4%, a recall rate of 70.2%, an accuracy rate of 76.3%, and an F1 score of 73.1%. Compared with the random forest model before optimization, the accuracy of the optimized model increased by 9.3%, the recall rate increased by 0.51, the accuracy rate increased by 9.1%, and the F1 score increased by 8%. These results show that the sparrow search algorithm has a significant effect in optimizing the random forest model, and can effectively improve the performance of the model in the task of TCM disease classification. This study not only verified the applicability of random forest model in TCM disease classification, but also improved the classification effect of the model through the introduction of sparrow search algorithm.
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