Enhancing Disease Prediction through Random Forests in Healthcare Analytics

This research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares Random Forests to other machine learning algorithms, such as Neural Networks and Support Vector Machines, and assesses their ability to forecast different illnesses. According to the findings, Random Forests have the ability to reliably forecast diseases since they provide greater accuracy, precision, recall, and F1-score. In addition, healthcare providers may benefit greatly from the model’s feature significance metrics, which help them understand which aspects have the most impact on illness outcomes. This study emphasises the revolutionary power of Random Forests to improve healthcare diagnoses, personalised treatment regimens, and patient care overall.