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

TL;DR

This paper explores enhancing Ayurvedic diagnosis by applying Multinomial Naive Bayes and K-modes clustering to classify overlapping Prakriti and Dosha types, achieving high accuracy and offering a more nuanced categorization.

Contribution

It introduces a novel classification of Doshas into seven overlapping categories using machine learning, improving upon traditional models that only consider three classes.

Findings

Achieved 0.90 accuracy in classifying Dosha types.

Demonstrated improved precision and F-score with the proposed methods.

Provided detailed analysis of seven overlapping clusters for better diagnosis.

Abstract

The identification of Prakriti types for the human body is a long-lost medical practice in finding the harmony between the nature of human beings and their behaviour. There are 3 fundamental Prakriti types of individuals. A person can belong to any Dosha. In the existing models, researchers have made use of SVM, KNN, PCA, Decision Tree, and various other algorithms. The output of these algorithms was quite decent, but it can be enhanced with the help of Multinomial Naive Bayes and K-modes clustering. Most of the researchers have confined themselves to 3 basic classes. This might not be accurate in the real-world scenario, where overlapping might occur. Considering these, we have classified 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 used contains a balanced set of all individual entries on which preprocessing steps of machine learning have been performed. Chi-Square test for handling categorical data is being used for feature selection. For model fitting, the method used in this approach is K-modes clustering. The empirical results demonstrate a better result while using the MNB classifier. All key findings of this work have achieved 0.90 accuracy, 0.81 precision, 0.91 F-score, and 0.90 recall. The discussion suggests a provident analysis of the seven clusters and predicts their occurrence. The results have been consolidated to improve the Ayurvedic advancements with machine learning.