Machine Learning Approaches for Predictive Analytics in Healthcare Systems

Authors

  • Sarah Chen Stanford University, Department of Computer Science
  • Michael Rodriguez Massachusetts Institute of Technology, AI Lab
  • Jennifer Wu University of California, Berkeley

Abstract

This study presents a comprehensive analysis of machine learning algorithms applied to healthcare data for predictive analytics. We evaluated several classification and regression models including Random Forests, Support Vector Machines, and Deep Neural Networks on a dataset of 50,000 patient records. Our results demonstrate that ensemble methods achieve 94.2% accuracy in predicting patient outcomes, outperforming traditional statistical approaches. The findings have significant implications for early disease detection and personalized treatment planning.

Published

2024-03-15

Issue

Vol. 1 No. 1 (2024): Inaugural Issue: Foundations of Innovation

Section

Research Articles

License

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY 4.0).

How to Cite

Machine Learning Approaches for Predictive Analytics in Healthcare Systems. (2024). International Journal of Science and Technology, 1(1), 1-15. https://assosiatech.com/index.php/ijst/article/view/1