Opis

• Tytuł:
  Ensemble machine learning methods to predict the balancing of ayurvedic constituents in the human body: Ensemble Machine Learning Methods to Predict
• Twórca:
  Rajasekar, Vani;
  Krishnamoorthi, Sathya;
  Saračević, Muzafer;
  Pepic, Dzenis;
  Zajmovic, Mahir;
  Zogic, Haris
• Opis:
  Ayurvedic medicines are categorized into seven constitutional forms ‘Prakriti’ which is a constituent in the Ayurvedic system of medicine to determine drought tolerance and drug responsiveness. Prakriti assessment entails a thorough physical examination as well as queries about physiological or behavioral characteristics. The prevalence of certain "doshas" is attributed by Ayurveda to the fundamental constituent of a person. Vata, pitta, and Kapha are the three main doshas mentioned. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags. The careful and appropriate analysis leads to an effective treatment. In this paper, we demonstrate the result of certain machine learning methods like Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), and Adaboost algorithm for various performance characteristics to predict human body constituencies. From the observations of results it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall of 0.97, precision and F-score of 0.96, the lower RSME value obtained is 0.64. The experimental results reveal that the improved model, which is based on ensemble learning methods, outperforms traditional methods significantly. According to the findings, advancements in the proposed algorithms could give machine learning a promising future.
• Dostępność obiektu:
  Dostęp otwarty
• Prawa:
  Copyright (c) 2021 Computer Science;
  https://creativecommons.org/licenses/by/4.0
• Data:
  2022.03.29
• Typ:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Język:
  angielski
• Źródło:
  2300-7036;
  1508-2806
• Format:
  application/pdf
• Wydawca:
  AGH University of Krakow, Faculty of Computer Science
• Relacja: https://journals.agh.edu.pl/csci/article/view/4315/2750
• Identyfikator: https://journals.agh.edu.pl/csci/article/view/4315 ;
  10.7494/csci.2022.23.1.4315
• Dostawca danych:
  AGH University of Science and Technology Journals
• Czy mogę z tego obiektu skorzystać?:
  poproś o pozwolenie
• Rodzaj zawartości:
  teksty