Ensemble-Based Big Data Analytics for Disease Prediction in Iot

The developing nexus between Internet of Things (IoT) technologies, healthcare, as well as analytics for prediction is the subject of this study. A deductive method is used to investigate how IoT-generated medical data and group learning approaches might be combined for predicting diseases. The study is guided by a descriptive research approach that emphasizes the necessity to thoroughly evaluate any possible overlap between these categories. This investigation's foundation is secondary data collecting from several IoT-enabled healthcare device sources. Studying the effects of picking features overall engineering, researchers found that doing so significantly increased the accuracy of models by 7% and precision by 8%. The evident superiority using ensemble methods is demonstrated by comparison with conventional models, having Random Forest surpassing logarithms approximately 15% in frequency. The study also assesses each model's efficiency of resources and scaling, emphasizing how practically applicable they are to high-throughput, practical applications. The results demonstrate the intriguing possibility of ensemble-based informatics for predicting illnesses using data provided by the Internet of Things. The report makes recommendations for further research, such as investigating cutting-edge ensemble approaches and incorporating other data modalities. To further improve and validate the suggested approach, undertaking real-world validation tests and incorporating methods for interpretation are advised. The ultimate goal of this research is to transform illness management overall patient outcomes by advancing medical predictive analytics.