Unleashing the Potential of Medicinal Plants: Efficacy Analysis for Chronic Diseases Using Svm Based Autoencoders

Abstract

This research paper presents an original approach to identifying and analyzing medicinal plants with potential efficacy in treating chronic diseases, focusing on Indian medicinal plants. Chronic diseases pose a significant global health challenge, and natural remedies derived from medicinal plants have gained recognition for their therapeutic potential. In this study, we propose a novel machine learning method to develop an effective analytic tool for Ayurvedic medical practitioners, aiding in their decision-making process. We propose a machine learning-based approach to analyze the efficacy of Indian medicinal plants for chronic diseases. Our method integrates traditional knowledge, scientific research, and clinical data, leveraging artificial intelligence algorithms to process and analyze large volumes of information. By utilizing a unique combination of natural language processing, data mining, and predictive modeling, our approach aims to uncover hidden patterns, correlations, and insights that can guide evidence-based recommendations for medicinal plant selection. The proposed analytic tool, developed using our novel machine learning method, assists Ayurvedic medical practitioners in making personalized treatment recommendations for chronic diseases. By considering factors such as patient demographics, disease characteristics, and plant properties, the tool provides tailored suggestions on the most suitable medicinal plants and their administration methods. The incorporation of machine learning enables continuous learning and improvement of the tool's recommendation system, allowing it to adapt to emerging research and evolving patient needs.