Leveraging XGBoost for Predictive Analytics in Healthcare: Enhancing Disease Diagnosis

In this study, we look at how XGBoost (Extreme Gradient Boosting) may be used in healthcare predictive analytics to improve the speed and precision of illness detection. Thanks to its built-in regularisation algorithms that avoid overfitting and its capacity to handle big datasets, XGBoost has proved useful in a broad variety of prediction applications. It is a scalable and high-performance machine learning algorithm. In order to improve patient outcomes, healthcare providers must ensure that patients are diagnosed with chronic and life-threatening conditions including cancer, heart disease, and diabetes as soon as possible. To test how well XGBoost performs in comparison to more conventional machine learning techniques, the research makes use of healthcare datasets collected from the actual world. The effectiveness of the model in predicting illness outcomes is evaluated using critical metrics including accuracy, precision, recall, and F1-score. Based on the findings, XGBoost is the best classifier for real-time healthcare applications because to its quicker processing efficiency and greater predictive ability. Additionally, the article delves into the model’s interpretability and how it may be integrated into clinical decision support systems. By demonstrating how XGBoost may improve diagnostic accuracy and bolster individualised treatment plans, this study adds to the expanding area of AI-driven healthcare.