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. 2024 Jun 10;19(6):e0303229.
doi: 10.1371/journal.pone.0303229. eCollection 2024.

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

Hossein Bibak  1 , Farzad Heydari  2 , Mohammad Sadat-Hosseini  3
Affiliations

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

Hossein Bibak et al. PLoS One. .

Abstract

The present study recorded indigenous knowledge of medicinal plants in Shahrbabak, Iran. We described a method using data mining algorithms to predict medicinal plants' mode of application. Twenty-oneindividuals aged 28 to 81 were interviewed. Firstly, data were collected and analyzed based on quantitative indices such as the informant consensus factor (ICF), the cultural importance index (CI), and the relative frequency of citation (RFC). Secondly, the data was classified by support vector machines, J48 decision trees, neural networks, and logistic regression. So, 141 medicinal plants from 43 botanical families were documented. Lamiaceae, with 18 species, was the dominant family among plants, and plant leaves were most frequently used for medicinal purposes. The decoction was the most commonly used preparation method (56%), and therophytes were the most dominant (48.93%) among plants. Regarding the RFC index, the most important species are Adiantum capillus-veneris L. and Plantago ovata Forssk., while Artemisia auseri Boiss. ranked first based on the CI index. The ICF index demonstrated that metabolic disorders are the most common problems among plants in the Shahrbabak region. Finally, the J48 decision tree algorithm consistently outperforms other methods, achieving 95% accuracy in 10-fold cross-validation and 70-30 data split scenarios. The developed model detects with maximum accuracy how to consume medicinal plants.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Flowchart of the proposed method.
Fig 2
Fig 2. The number of medicinal species in each family.
Fig 3
Fig 3. Percentage of used plant parts.
Fig 4
Fig 4. Modes of preparations used of medicinal plants for treatment.
Fig 5
Fig 5. Life grown forms of wild medicinal species from Shahrbābak area.
Fig 6
Fig 6. Percentage of species and citation in each medicinal use category.
Fig 7
Fig 7. Plants with the highest number of use report.
Fig 8
Fig 8. The liner regression model between informant’s age and number of plant species uses.
Fig 9
Fig 9. The amount of dispersion of samples of each class based on the class of "mode of application".
Fig 10
Fig 10. Comparison between accuracy through model training based on cross-validation and 70–30 split.
See this image and copyright information in PMC

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