A method used to determine the precise sequence of nucleotides in a piece of DNA, allowing for high-throughput genetic analysis.
Published in Chapter:
Advancing Precision Medicine: Integrating AI and Machine Learning for Personalized Healthcare Solutions
Bhuvaneswari R. (Amrita Vishwa Vidyapeetham, Chennai, India),
Prabu M. (Amrita Vishwa Vidyapeetham, Chennai, India), Diviya M. (Amrita Vishwa Vidyapeetham, Chennai, India), Subramanian M. (St. Joseph's College of Engineering, Chennai, India), and
Arul Kumar Natarajan (Samarkand International University of Technology, Samarkand, Uzbekistan)
Copyright: © 2024
|Pages: 18
DOI: 10.4018/979-8-3693-7462-7.ch015
Abstract
Precision medicine, also known as personalized medicine, aims to tailor medical care to individual characteristics, genetic information, and lifestyle for more accurate disease risk predictions and personalized therapies. Traditional methods in precision medicine, such as clinical assessments, laboratory testing, and pathology testing, can be enhanced with AI models to improve accuracy, precision, and personalization. Genomic analysis, disease prediction, drug discovery, and imaging analysis are key components of precision medicine. Wearable devices support continuous monitoring for proactive intervention. ML algorithms like random forest and K-means clustering are used for prediction and early diagnosis of heart disease. A deep learning model for Alzheimer's disease diagnosis and a recommended application for maintaining health details are also suggested. Recursive feature elimination is used in disease prediction and treatment policy for diabetes.