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Article type: Research Article
Authors: Majumder, Saibala | Kutum, Rintub | Khatua, Debnarayanc | Sekh, Arif Ahmedd | Kar, Samarjite; f; * | Mukerji, Mitalig | Prasher, Bhavanah
Affiliations: [a] Department of Computer Science and Engineering (Data Science), Dr. B.C. Roy Engineering College, Durgapur | [b] Department of Computer Science, Ashoka University, Haryana | [c] Department of Mathematics, Vignan’s Foundation for Science, Technology & Research, Andhra Pradesh | [d] School of Computer Science, XIM University, Bhubaneshwar | [e] Department of Mathematics, National Institute of Technology Durgapur | [f] Department of Graphical Systems, Vilnius Gediminas Technical University, Lithuania | [g] Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur | [h] Ayurgenomics Unit-TRISUTRA, CSIR-Institute of Genomics and Integrative Biology, New Delhi
Correspondence: [*] Corresponding author. Samarjit Kar, E-mail: samarjit.kar@maths.nitdgp.ac.in.
Abstract: Predictive medicine for a holistic and proactive approach to health management is steadily replacing the reactive healthcare model as the dominant paradigm in the twenty-first century. The Ayurvedic medical system, which incorporates all parts of predictive medicine, divides people into seven constitution types, or Prakriti, to help practitioners determine their initial homeostatic conditions. This article uses data on the phenotypic characteristics of 217 healthy people who fall into three extreme Prakriti types to conduct a study for predicting Prakriti classes. Those who fit the Prakriti type are drawn from two genetically different northern and western India cohorts. In order to dichotomize inter-individual variability in various individuals, eight machine learning (ML) classifiers are used. The prediction skills of the ML algorithms are evaluated here using ten pairs of predefined training and testing datasets for each cohort. Lastly, a performance comparison of various ML algorithms is carried out using six crucial performance criteria. The study aims to investigate and appraise using artificial intelligence (AI) to evaluate Prakriti in Ayurveda. The use of AI in Prakriti assessment may have several advantages, including enhancing the consistency and accuracy of assessments and minimizing reliance on subjective judgements. This study aims to further our knowledge of how technology can be applied to enhance the practice of Ayurveda and possibly improve patient outcomes.
Keywords: Ayurveda, Ayurgenomics, classification, performance metrics
DOI: 10.3233/JIFS-220990
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 9827-9844, 2023
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