Abstract
Artificial intelligence (AI) based disease identification has the potential to transform medicine by utilizing machine learning algorithms and techniques to analyze large volumes of medical data and identify patterns and features that may be difficult for human experts to detect. However, there are still challenges and limitations to overcome, such as the need for high-quality medical data and concerns around privacy and bias. This chapter explores the growing intersection of machine learning (ML) and AI techniques with disease prediction. The chapter begins by providing an overview of ML and AI methodologies commonly employed in disease prediction, including supervised and unsupervised learning algorithms, deep learning techniques, and ensemble methods. Lastly, the chapter outlines future directions and research opportunities in the field.
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