Comparative Analysis of Machine Learning Algorithms in Enhancing Healthcare Outcomes

Abstract

The relentless growth of data, accelerated since the industrial revolution and now amplified by the digital age, presents unprecedented opportunities and challenges for the healthcare industry. As the global datasphere is projected to expand from 33 zettabytes to 175 zettabytes between 2018 and 2025, leveraging this data through advanced machine learning (ML) algorithms has become crucial, especially in the wake of the COVID-19 pandemic. This study explores the integration of ML and data analytics in healthcare, demonstrating their potential to revolutionize patient care, disease diagnosis, treatment personalization, administrative efficiency, and drug development. By utilizing various ML algorithms, including Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, on a diverse dataset, this research evaluates their performance in predicting health outcomes and improving healthcare services. Random Forest and KNN algorithms showed superior performance in accuracy and reliability, highlighting the importance of selecting appropriate models based on dataset characteristics. The findings underscore the transformative potential of ML and data analytics in healthcare, emphasizing the need for robust datasets, ethical considerations, and data security to maximize their benefits. This integration promises to enhance proactive care, optimize resource allocation, and personalize medical treatments, ultimately leading to improved patient outcomes and operational efficiencies.