Precision Medicine and Personalized Healthcare: Interdisciplinary Approaches for Enhanced Patient Outcomes

Abstract

Precision medicine and personalized healthcare, so to speak, represent a radical break in conventional clinical practice and medical treatment according to the characteristics of each patient. In this research, through the use of machine learning algorithms integration with biomedical data, it aims at improving diagnostic accuracy and therapeutic effectiveness by using interdisciplinary approaches. To assess the predictive performance of four machine learning models (Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), gradient boosting), they were applied to a curated healthcare dataset. At the highest accuracy, Gradient Boosting algorithm became the highest with 94.6, Random Forest with 92.1, SVM with 89.4, and KNN with 86.7. These results suggest an ensemble and margin-based classifiers are very successful for precision diagnostics. Realized were experimental evaluations of critical performance metrics of AI models as guides to individualized treatment plans. The subsequent review of recent literature further highlights the expanding place of AI in radiogenomics, nanoparticle medication conveyance and microbiota based diagnostics. As this study shows, grouping machine learning into clinical workflows not only improves decision making accuracy but also opens the door to more medically equitable and effective outcomes.