Mapping of soil suitability for medicinal plants using machine learning methods

Inadequate conservation of medicinal plants can affect their productivity. Traditional assessments and strategies are often time-consuming and linked with errors. Utilizing herbs has been an integral part of the traditional system of medicine for centuries. However, its sustainability and conservation are critical due to climate change, over-harvesting and habitat loss. The study reveals how machine learning algorithms, geographic information systems (GIS) being a powerful tool for mapping and spatial analysis, and soil information can contribute to a swift decision-making approach for actual forethought and intensify the productivity of vulnerable curative plants of specific regions to promote drug discovery. The data analysis based on machine learning and data mining techniques over the soil, medicinal plants and GIS information can predict quick and effective results on a map to nurture the growth of the herbs. The work incorporates the construction of a novel dataset by using the quantum geographic information system tool and recommends the vulnerable herbs by implementing different supervised algorithms such as extra tree classifier (EXTC), random forest, bagging classifier, extreme gradient boosting and k nearest neighbor. Two unique approaches suggested for the user by using EXTC, firstly, for a given subregion type, its suitable soil classes and secondly, for soil type from the user, its respective subregion labels are revealed, finally, potential medicinal herbs and their conservation status are visualised using the choropleth map for classified soil/subregion. The research concludes on EXTC as it showcases outstanding performance for both soil and subregion classifications compared to other models, with an accuracy rate of 99.01% and 98.76%, respectively. The approach focuses on serving as a comprehensive and swift reference for the general public, bioscience researchers, and conservationists interested in conserving medicinal herbs based on soil availability or specific regions through maps.

Machine learning strengthened prediction of tracheal, bronchus, and lung cancer deaths due to air pollution

This work pointed out the use of machine learning tools to predict the effect of CO, O3, CH4, and CO2 on TBL (tracheal, bronchus, and lung cancer) deaths from 1990 to 2019. In this study, data from 203 countries/locations were used. We used evaluation metrics like accuracy, area under curve (AUC), recall, precision, and Matthews correlation coefficient (MCC) to determine the prediction efficiency of the models. The models that yielded accuracy between 89 and 90 were selected in this study. The essential features in the prediction process were extracted, and it was found that CO influenced the prediction process. Extra trees classifier, random forest classifier, gradient boosting classifier, and light gradient boosting machine were selected from 14 other classifiers based on the accuracy metric. The best-performing models, according to our benchmark standards, are the extra trees classifier (90.83%), random forest classifier (89.17%), gradient boosting classifier (89.17%), and light gradient boosting machine (89.17). We conclude that machine learning models can be used in predicting mortality, i.e., the number of deaths, and could assist us in predicting the role of air pollutants on TBL deaths globally.