Big Data Analytics for Disease Prediction: Leveraging Ensemble Models in Healthcare Informatics

In healthcare, where the amount and complexity of medical data is constantly increasing, traditional analytical methods are finding it increasingly difficult to handle the problem of enhancing the accuracy of disease prediction. We present a strategy to improve the prognosis of long-term illnesses including cancer, diabetes, and heart disease by combining big data analytics with ensemble learning techniques. Using large clinical datasets and ensemble models like XGBoost, Random Forest, and Gradient Boosting Machines (GBM), we show that ensemble models are better than individual classifiers in recall, precision, and accuracy. Scalability in big data platforms, such as Apache Spark, also allows for quick model training and analytics in real-time. The results demonstrate that ensemble models are very useful for illness prediction and have great promise for enhancing healthcare informatics decision assistance.