On intelligent Prakriti assessment in Ayurveda: a comparative study

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.