Diversity, Distribution Pattern and Conservation Status of the Plants Used in Liver Diseases/Ailments in Indian Himalayan Region

In the Indian Himalayan Region, th studies focused on diversity of the plants used fo treating liver diseases/ailments have not been carried out so far. Therefore, the present attempt has been made to study the diversity, distribution pattern and conservation status of the plant species used fo treating liver diseases/ailments in that region. A tota of 138 species (35 species of trees, 22 shrubs and 8 herbs) belonging to 98 genera in 60 families hav been recorded. Amongst the families, Euphorbiacea (9 species), and altitudinal zone <1,800 m, (i.e., 11 species) are rich in species. Traditionally, variou plant parts, such as roots/rhizomes/tubers (46 species), leaves (31), whole plants (30), barks (15) fruits (13), seeds and unspecified parts (8 each), and inflorescence (1) are used for the treatment of live diseases/ailments. 34 species are native, 3 ar endemic and 15 near endemic. 7 species ar categorized as Critically Endangered (Betula utilis) Endangered (Podophyllum hexandrum, Ephedra gerardiana, and Nardostachys grandiflora) and Vulnerable (Bergenia ligulata, B. stracheyi, and Hedychium spicatum) using new IUCN criteria Available chemical composition of plant parts used fo the treatment of liver diseases/ailments have beengiven. Assessment of the populations of threatened species, development of an appropriate strategy, action plan for the conservation and sustainable utilization of such components of plant diversity are suggested.

Liver Ailment Prediction Using Random Forest Model

Today,liver disease,or any deterioration in one’s ability to survive,is extremely common all around the world.Previous research has indicated that liver disease is more frequent in younger people than in older ones.When the liver’s capability begins to deteriorate,life can be shortened to one or two days,and early prediction of such diseases is difficult.Using several machine learning(ML)approaches,researchers analyzed a variety of models for predicting liver disorders in their early stages.As a result,this research looks at using the Random Forest(RF)classifier to diagnose the liver disease early on.The dataset was picked from the University of California,Irvine repository.RF’s accomplishments are contrasted to those of Multi-Layer Perceptron(MLP),Average One Dependency Estimator(A1DE),Support Vector Machine(SVM),Credal Decision Tree(CDT),Composite Hypercube on Iterated Random Projection(CHIRP),K-nearest neighbor(KNN),Naïve Bayes(NB),J48-Decision Tree(J48),and Forest by Penalizing Attributes(Forest-PA).Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error(RRSE),Root Mean Squared Error(RMSE),accuracy,recall,precision,specificity,Matthew’s Correlation Coefficient(MCC),F-measure,and G-measure.RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328,however,its percentage accuracy is 72.1739.The widely acknowledged result of this work can be used as a starting point for subsequent research.As a result,every claim that a new model,framework,or method enhances forecastingmay be benchmarked and demonstrated.