Using Data Mining to Uncover Traditional Medicinal Plant Knowledge

Jim Crocker
11th June, 2024

Using Data Mining to Uncover Traditional Medicinal Plant Knowledge

Image Source: Natural Science News, 2024

Key Findings

  • The study in Shahrbabak, Iran, documented 141 medicinal plants from 43 botanical families, with Lamiaceae being the most dominant
  • Leaves were the most commonly used plant part, and decoction was the most frequent preparation method, used in 56% of cases
  • The J48 decision tree algorithm was the most effective, achieving 95% accuracy in predicting the mode of application for medicinal plants
The recent study conducted by the University of Jiroft in Shahrbabak, Iran, recorded indigenous knowledge of medicinal plants and utilized data mining algorithms to predict their mode of application[1]. This research is significant as it addresses the need to document traditional medicinal practices, preserving valuable ethnopharmacological knowledge that may otherwise be lost. In this study, 21 individuals aged 28 to 81 were interviewed, and data were collected and analyzed using various quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). The study documented 141 medicinal plants from 43 botanical families, with Lamiaceae being the most dominant family, represented by 18 species. Leaves were the most frequently used plant part for medicinal purposes, and decoction was the most common preparation method, accounting for 56% of the reported uses. Therophytes (annual plants) were the most dominant life form among the documented species, comprising 48.93%. To analyze the data, the researchers employed several classification algorithms, including support vector machines, J48 decision trees, neural networks, and logistic regression. Among these, the J48 decision tree algorithm consistently outperformed the others, achieving 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. For instance, a study in the Eastern Ghats of southern India documented 118 plant species used by the Malayali tribal people for medicinal purposes[2]. Similar to the findings in Shahrbabak, the Eastern Ghats study highlighted the importance of documenting traditional knowledge and identified key species for further ethnopharmacological studies. Both studies emphasize the cultural significance and medicinal value of local flora, although they focus on different geographic regions and plant species. Another relevant study investigated the medicinal plant use in the Monti Sicani Regional Park in Sicily, Italy[3]. This research documented 108 wild species used for medicinal purposes and highlighted the ongoing process of cultural erosion in the region. The findings from Shahrbabak complement this study by demonstrating the continued relevance and application of traditional medicinal knowledge in different parts of the world. The use of quantitative indices and data mining algorithms in the Shahrbabak study represents a significant advancement in ethnopharmacological research. The application of these methods allows for a more systematic and accurate analysis of traditional knowledge, facilitating the identification of key medicinal plants and their uses. This approach aligns with the guidelines established by the Consensus Statement on Ethnopharmacological Field Studies (ConSEFS), which emphasizes the importance of adhering to well-defined quality standards and reproducible methods in ethnopharmacological research[4]. In conclusion, the study conducted by the University of Jiroft in Shahrbabak provides valuable insights into the traditional medicinal practices of the region and demonstrates the potential of data mining algorithms in ethnopharmacological research. By documenting and analyzing indigenous knowledge, this study contributes to the preservation of cultural heritage and the discovery of new medicinal applications for plants.

MedicineBiochemPlant Science

References

Main Study

1) Exploring the power of data mining for uncovering traditional medicinal plant knowledge: A case study in Shahrbabak, Iran.

Published 10th June, 2024

https://doi.org/10.1371/journal.pone.0303229

Related Studies

2) An ethnobotanical study of medicinal plants in Palamalai region of Eastern Ghats, India.

https://doi.org/10.1016/j.jep.2015.05.046

3) Ethnobotanical investigation on wild medicinal plants in the Monti Sicani Regional Park (Sicily, Italy).

https://doi.org/10.1016/j.jep.2014.02.032

4) Best practice in research: Consensus Statement on Ethnopharmacological Field Studies - ConSEFS.

https://doi.org/10.1016/j.jep.2017.08.015


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