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Predictive Analysis of Medicinal Plants for Central Nervous System Diseases Using Machine Learning
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Author(s): Sasikaladevi N. (SASTRA University, India), Geetha Krishnan (SASTRA University, India)and S. Aarthi (SASTRA University, India)
Copyright: 2025
Pages: 26
Source title:
Transforming Neuropsychology and Cognitive Psychology With AI and Machine Learning
Source Author(s)/Editor(s): Rohit Bansal (Stanford Institute of Management and Technology, Australia), Tariq Maqableh (Charles Sturt University, Australia), Gunjan Shuklaa (SICA College, Indore, India), Fazla Rabby (Stanford Institute of Management and Technology, Australia)and Remya Lathabhavan (Indian Institute of Management, Bodh Gaya, India)
DOI: 10.4018/979-8-3693-9341-3.ch012
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Abstract
Central nervous system (CNS) diseases have witnessed an alarming rise globally, affecting millions of individuals and imposing significant healthcare expenses. Traditional medicinal plants have played a pivotal role in addressing various ailments, including CNS disorders. In this research, we propose a cutting-edge machine learning approach to accurately predict the effectiveness of medicinal plants in treating CNS diseases. Leveraging the VNPlant200 dataset comprising plant images and associated metadata, we train a state-of-the-art convolutional neural network (CNN) to extract profound features. To amplify the discriminative power of these features, we employ matrix-based discriminant analysis, thereby augmenting our model's predictive capabilities. Furthermore, we integrate an ensemble technique that combines multiple classifiers, resulting in improved accuracy with a remarkable rate of 100%. Additionally, we have developed a user-friendly mobile application empowering individuals to identify and classify medicinal plants based on their potential for treating CNS diseases.
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