In a groundbreaking study, researchers from the University of Jiroft in Shahrbabak, Iran, have successfully documented indigenous knowledge of medicinal plants and employed data mining algorithms to predict their mode of application. This research, published on June 10th, 2024, addresses the critical need to preserve valuable ethnopharmacological knowledge that may otherwise be lost.
The study involved interviewing 21 individuals aged 28 to 81 and collecting data on their traditional medicinal practices. The researchers analyzed the data using various quantitative indices, including the informant consensus factor, the cultural importance index, and the relative frequency of citation.
The study documented an impressive 141 medicinal plants from 43 botanical families, with Lamiaceae emerging as the most dominant family, represented by 18 species. Leaves were found to be the most frequently used plant part for medicinal purposes, while decoction was the most common preparation method, accounting for 56% of the reported uses. Interestingly, therophytes, annual plants, were the most dominant life form among the documented species, comprising 48.93%.
To analyze the collected data, the researchers employed several classification algorithms, such as support vector machines, J48 decision trees, neural networks, and logistic regression. The J48 decision tree algorithm consistently outperformed the others, achieving an impressive 95% accuracy in 10-fold cross-validation and 70-30 data split scenarios. This model effectively predicts the mode of application for medicinal plants, providing a robust tool for understanding and utilizing traditional medicinal knowledge.
The RFC index identified Adiantum capillus-veneris L. and Plantago ovata Forssk. as the most important species in the Shahrbabak region, while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index revealed that metabolic disorders are the most commonly treated ailments using these plants.
This study builds upon previous ethnopharmacological research conducted in other regions, such as the Eastern Ghats of southern India and the Monti Sicani Regional Park in Sicily, Italy. These studies highlight the importance of documenting traditional knowledge and identifying key species for further ethnopharmacological studies, emphasizing the cultural significance and medicinal value of local flora.
The Shahrbabak study represents a significant advancement in ethnopharmacological research by employing quantitative indices and data mining algorithms. This approach allows for a more systematic and accurate analysis of traditional knowledge, facilitating the identification of key medicinal plants and their uses. The study aligns with the guidelines established by the Consensus Statement on Ethnopharmacological Field Studies, which emphasizes the importance of adhering to well-defined quality standards and reproducible methods in ethnopharmacological research.
By documenting and analyzing indigenous knowledge, the study conducted by the University of Jiroft in Shahrbabak contributes to the preservation of cultural heritage and the discovery of new medicinal applications for plants. This research serves as a valuable resource for future ethnopharmacological studies and highlights the potential of data mining algorithms in uncovering traditional medicinal plant knowledge.
