Paper Key : IRJ************146
Author: Maroju Deepika
Date Published: 11 Jul 2024
Abstract
The deployment of machine learning and artificial intelligence methods has significantly enhanced the automation and transformation of illness detection and diagnosis in various medical and agricultural areas. Complex algorithms like logistic regression, convolutional neural networks (CNNs), and support vector machines (SVMs) have been employed to detect and diagnose diseases such as COVID-19, Alzheimer's, Parkinson's, heart disease, and cancer. Research has shown that hybrid models, which combine multiple techniques like CNNs and SVMs, often achieve higher efficiency and accuracy compared to their components. AI's potential extends beyond healthcare, with innovative methods facilitating the early detection of agricultural diseases. For instance, federated learning enables the privacy-preserving training of AI models across decentralized devices. The promise of machine learning and AI in revolutionizing healthcare, including disease diagnosis, treatment, and prevention, is vast and well-supported by numerous studies. These advancements could lead to significant improvements in health outcomes and potentially save lives. As these technologies evolve, we can anticipate even more groundbreaking applications and innovations
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