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The identification of Prakriti types for the human body is a long-lostmedical practice in finding the harmony between the nature of human beings andtheir behaviour. There are 3 fundamental Prakriti types of individuals. Aperson can belong to any Dosha. In the existing models, researchers have madeuse of SVM, KNN, PCA, Decision Tree, and various other algorithms. The outputof these algorithms was quite decent, but it can be enhanced with the help ofMultinomial Naive Bayes and K-modes clustering. Most of the researchers haveconfined themselves to 3 basic classes. This might not be accurate in thereal-world scenario, where overlapping might occur. Considering these, we haveclassified the Doshas into 7 categories, which includes overlapping of Doshas.These are namely, VATT-Dosha, PITT-Dosha, KAPH-Dosha, VATT-PITT-Dosha,PITT-KAPH-Dosha, KAPH-VATT-Dosha, and VATT-PITT-KAPH-Dosha. The data usedcontains a balanced set of all individual entries on which preprocessing stepsof machine learning have been performed. Chi-Square test for handlingcategorical data is being used for feature selection. For model fitting, themethod used in this approach is K-modes clustering. The empirical resultsdemonstrate a better result while using the MNB classifier. All key findings ofthis work have achieved 0.90 accuracy, 0.81 precision, 0.91 F-score, and 0.90recall. The discussion suggests a provident analysis of the seven clusters andpredicts their occurrence. The results have been consolidated to improve theAyurvedic advancements with machine learning.

Enhancing Ayurvedic Diagnosis using Multinomial Naive Bayes and K-modes Clustering: An Investigation into Prakriti Types and Dosha Overlapping