Advancing Precision Medicine

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.