Enhancing Disease Prediction through Ensemble Learning Techniques

The study demonstrates how technology has had a dramatic influence on healthcare, allowing for the analysis of large clinical datasets using machine learning for early illness identification. Chronic disorders such as cardiovascular disease (CVD), Chronic renal illness, and Chronic Mellitus Diabetes offer significant global health issues, using existing diagnostic procedures that are time-consuming and error-prone. The report emphasizes the rising burden of chronic illnesses, estimating that 73% of deaths will be related to these ailments by 2025. Recognizing the links between illnesses such as CKD and CVD is critical for optimal care, with a particular emphasis on the increased prevalence and health hazards associated with Chronic Diabetes Mellitus (CDM). The fundamental goal of the research is to create an effective prediction model for early illness diagnosis by employing an adaptable machine learning model that can adapt to a variety of datasets. The review of the literature looks at several machine learning algorithms that have been utilized in prior research, emphasizing their potential for automating illness identification. The methods section describes the procedures required in collecting data, preparing it, and modeling it using ensemble learning. Metrics of performance evaluation confirm the Extra tree classifier's advantage and it performs the best among all the four ensemble techniques and improves the accuracy of predicting diseases. Finally, the above investigation highlights the promise of computational learning in solving the complex issues faced by chronic illnesses, emphasizing the crucial need for accurate early disease prediction in the context of a growing global health crisis.