Research Article
Heart Disease Diagnosis and Diet Recommendation System Using Ayurvedic Dosha Analysis
@ARTICLE{10.4108/eetiot.6016, author={Kuldeep Vayadande and Chudaman D. Sukte and Yogesh Bodhe and Tanishka Jagtap and Atharv Joshi and Palash Joshi and Arushi Kadam and Sai Kadam}, title={Heart Disease Diagnosis and Diet Recommendation System Using Ayurvedic Dosha Analysis}, journal={EAI Endorsed Transactions on Internet of Things}, volume={11}, number={1}, publisher={EAI}, journal_a={IOT}, year={2024}, month={12}, keywords={Ayurveda, Disease diagnosis, Dosha analysis, Machine learning, Nadi Parikshan}, doi={10.4108/eetiot.6016} }- Kuldeep Vayadande
Chudaman D. Sukte
Yogesh Bodhe
Tanishka Jagtap
Atharv Joshi
Palash Joshi
Arushi Kadam
Sai Kadam
Year: 2024
Heart Disease Diagnosis and Diet Recommendation System Using Ayurvedic Dosha Analysis
IOT
EAI
DOI: 10.4108/eetiot.6016
Abstract
The current healthcare system often fails to account for individual health needs, leading to ineffective preventive measures and dietary guidance. Ayurvedic principles, which focus on the Dosha, offer a profound understanding of an individual's constitution, influencing their health, vulnerability to specific diseases, and ideal dietary choices. This paper explores the evolving intersection of ancient Ayurvedic wisdom and modern technology in the realm of disease diagnosis. Ayurveda, with its emphasis on personalized well-being, has long been a source of holistic health practices. In this context, the study delves into the intricate system of Ayurvedic Dosha analysis and its potential applications in contemporary healthcare. The research introduces an innovative way that seamlessly integrates traditional Ayurvedic pulse examination with state-of-the-art technology. By employing pulse sensors and advanced algorithms, the system not only identifies specific ailments but also classifies patients into Ayurvedic Prakriti types. Going beyond conventional diagnosis, this holistic approach extends to personalized recommendations, encompassing diet, lifestyle, Ayurvedic treatments, exercise, and daily routines. While addressing the challenges of harmonizing ancient principles with modern technology, the paper also presents the performance metrics of the model. The accuracy rates are as follows: Logistic Regression (LR) - 85.94%, Random Forest - 89.21%, Decision Tree - 99.70%, and k-Nearest Neighbors (KNN) - 86.43%. These metrics underscore the robustness of the system. In addition to outlining core concepts, methodologies, and model accuracies, the study explores current trends and recent developments in the field, offering readers a comprehensive understanding of Ayurvedic Dosha-based disease diagnosis. The research contributes to the broader discourse on healthcare by paving the way for early detection and individualized, holistic well-being for patients.
Copyright © 2024 K. Vayadande et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
