The analysis and systematic monitoring/observance of healthcare related data to identify trends, patterns, and outbreaks of diseases within a population. Disease surveillance ensures to track the spread of disease, assess public health risks and guide preventive measures.
Published in Chapter:
Introduction to Predictive Analysis in Healthcare: From Data to Diagnosis - Exploring the Potential of Predictive Analytics
Marwana Sayed (School of Engineering, Ajeenkya D.Y. Patil University, India),
Smita Shahane (School of Engineering, Ajeenkya D.Y. Patil University, India),
Nikhil Mane (School of Engineering, Ajeenkya D.Y. Patil University, India), and
Manavi Gilotra (School of Engineering, Ajeenkya D.Y. Patil University, India)
Copyright: © 2024
|Pages: 26
DOI: 10.4018/979-8-3693-3629-8.ch003
Abstract
This chapter addresses how machine learning (ML) and artificial intelligence (AI) are revolutionizing the field of healthcare disease prediction. It describes how to employ public information and machine learning algorithms to forecast conditions including osteoarthritis, breast cancer, and Alzheimer's. Case studies from real-life scenarios show how predictive models work effectively for early diagnosis and customized responses. Critical analysis is done on ethical issues such as model interpretability and patient privacy. The chapter places a strong emphasis on the necessity of ethical frameworks and competent data handling to direct the incorporation of ML into healthcare. This chapter adds to the continuing discussion on AI's role in healthcare by providing a thorough analysis of disease prediction in osteoarthritis, Alzheimer's, and breast cancer, promoting better patient care and public health outcomes.