From Data to Diagnosis: How Algorithm Selection Drives Machine Learning Success in Healthcare

Abstract

As healthcare increasingly adopts data-driven approaches, selecting the right machine learning (ML) algorithms is vital for accurate and effective medical diagnostics. This paper analyzes the impact of algorithm choice on healthcare outcomes, focusing on decision trees, neural networks, support vector machines, and ensemble methods. We explore each algorithm's strengths, limitations, and suitability for tasks like disease detection, treatment recommendations, and predictive analytics. Key considerations such as data quality, interpretability, and computational efficiency are also discussed. We conclude that aligning algorithm selection with clinical needs is essential for the success of ML applications in healthcare.

Ähnliche Arbeiten