AI-Driven Personalized Healthcare System and Predictive Analytics for Precision Medicine

Advancement of artificial intelligence (AI), machine learning (ML), and health care analytics has provided novel ways to personalize healthcare and implement precision medicine. This study suggests developing an AI-based personalized healthcare system based on analysis of patients' demographic details, health care history, lab results, genomic information, and current health parameters to predict their disease risks and make personalized treatment decisions. For implementation of this concept, machine learning models such as Random Forest, XGBoost, Support Vector Machine, and neural network will be applied to analyze various data related to health care and identify patterns related to disease development and its treatment. Predictive analysis will be performed to determine patients' risk scores, probability of disease and treatment outcomes. Moreover, the use of explainable AI will enhance the transparency of predictions made. The proposed system will aim at 90-95% accuracy of predictions, about 30-40% decrease in time required to make diagnostics decisions and about 25-35% better early-risk detection than traditional approaches.