Transforming the Prognosis of Chronic Illnesses: Using Machine and Ensemble Learning to Provide Precision Healthcare

Chronic disease represent a global health concern that impacts families, individuals, and healthcare systems. The "Smart Healthcare" era's introduction of cutting-edge technologies has created new opportunities for the management and prognosis of chronic illnesses. The use of ensemble learning approaches to predict long-term health outcomes for common disorders such hepatic carcinoma, cataracts, colorectal cancer, gastrointestinal neoplasms, pulmonary cancer, and cardio- cerebrovascular diseases is investigated in this work. The study assesses how well ensemble learning models predict the course of chronic diseases using a variety of datasets. The Google Collaborator platform was used to carry out the experiments in a controlled, simulated setting. The results highlight how these prediction models can improve healthcare tactics by offering actionable insights, enabling more effective interventions and decision-making.