Enhancing Multi-Disease Prediction with Advanced Machine Learning Techniques for Early Diagnosis and Improved Healthcare Outcomes

The idea of diagnosing diseases with machine learning has revolutionized healthcare by allowing for extremely precise and prompt diagnosis. The capacity to concurrently forecast several illnesses, such as diabetes, Parkinson's disease, liver disease, and heart disease, makes early detection and treatment easier, increasing patient survival rates and lowering overall medical expenses. This research contributes to improved health outcomes by improving disease prediction, which is in line with the UN Sustainable Development Goal 3 (Good Health and Well-Being). Furthermore, it advances healthcare systems by utilizing cutting-edge technologies, supporting SDG 9 (Industry, Innovation, and Infrastructure) and SDG 10 (Reduced Inequalities) by facilitating access to accurate diagnostics. This study examines the potential, drawbacks, and viability of using machine learning for disease modeling and prognosis. In this study, the potential, constraints, and viability of using machine learning for disease modeling and prognosis are examined. To improve predicted accuracy, we examine popular classification algorithms, illness evaluation data sources, and methods like feature selection, model evaluation, and multimodal data fusion. The results of this study show how machine learning may significantly improve population health by increasing healthcare accessibility and efficiency while also supporting global sustainability goals.