Abstract

Abstract

Machine learning has shown great potential in healthcare by improving diagnostic accuracy, predicting disease outcomes, and personalizing treatment plans. Among the various algorithms, Random Forests are particularly notable for their robustness and accuracy. This research focuses on enhancing Random Forest algorithms to make them more effective in healthcare applications. By addressing the limitations of Decision Trees and exploring advanced techniques such as hyperparameter tuning, feature selection, and adaptive methods, the study aims to develop more reliable and precise diagnostic tools. The research includes a thorough review of existing literature, mathematical analysis, and practical applications across different fields. Experimental results indicate that hyperparameter tuned Random Forest models significantly outperform single Decision Trees and Random Forests, offering better generalization and accuracy. These enhanced models are expected to lead to earlier and more accurate diagnoses, improved patient outcomes, and more efficient resource utilization in the healthcare sector.

Keywords: Random Forest Algorithm, Hyperparameter Tuning, Bagging, Feature Selection, Machine Learning in Healthcare