Ensemble machine learning methods to predict the balancing of ayurvedic constituents in the human body

• Autorzy: Rajasekar Vani , Krishnamoorthi Sathya , Saracevic Muzafer , Pepic Dzenis , Zajmovic Mahir , Zogic Haris

Identyfikatory

DOI

10.7494/csci.2022.23.1.4315

• Języki publikacji: EN

Abstrakty

EN

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 neighbor (KNN), artificial neural network (ANN), and AdaBoost algorithms for various performance characteristics to predict human body constituencies. 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 to predict human body constituencies. From an observation of the results, it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall (0.97), precision and F-score (0.96), and lower RSME values (0.64). The experimental results reveal that the improved model (which is based on ensemble-learning methods) significantly outperforms traditional methods. According to the findings, advancements in the proposed algorithms could give machine learning a promising future.

Słowa kluczowe

EN

machine learning; artificial neural networks; diagnose; Ayurveda constituent; support vector machine

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2022
• Tom: T. 23 (1)
• Strony: 117--132
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Rajasekar Vani

• Kongu Engineering College, Department of CSE,India

autor

Krishnamoorthi Sathya

• Kongu Engineering College, Department of CT/UG, India

autor

Saracevic Muzafer

• University of Novi Pazar, Department of Computer Sciences, Serbia

• https://orcid.org/0000-0003-2577-7927

autor

Pepic Dzenis

• University of Novi Pazar, Department of Computer Sciences, Serbia

autor

Zajmovic Mahir

• University VITEZ Travnik, Faculty of Information Technology, Bosnia and Herzegovina

autor

Zogic Haris

• University of Novi Pazar, Department of Economic Sciences, Serbia

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).