Personalized Healthcare Systems Using AI

AI-based integration in personalized healthcare systems is expected to improve patient outcomes significantly using precision medicine and advanced diagnostics. This research will analyze four types of AI-based algorithms for analyzing the healthcare data base: Random Forest, Support Vector Machines (SVM), Logistic Regression, and K-Nearest Neighbors (KNN). The following study was conducted on a patient record dataset to check the models: accuracy scores 92.3% highest score for Random Forest, followed by SVM with an accuracy score of 90.8%. The Logistic Regression model produced 86.5% accuracy and demonstrated to be excellent with interpretability, while KNN lagged at an accuracy of 82.7%. The results show that Random Forest and SVM are suitable for being applied to complex, multi-dimensional healthcare data, therefore resulting in greater generalizability and greater predictive power. However, Logistic Regression is useful and transparent in decision-making in healthcare. The study proves the point that advances made in AI must be balanced with human interpretability to preserve the ethical and understandable nature of AI-based healthcare systems. The results remind the necessity for revolutionizing personalized health care through delivering more accurate, timely, and tailored care.