Predictive Healthcare Machine Learning Models that are Easy to Understand: Finding the Right Balance Between Precision and Openness in Clinical Prognoses

Predicting patient outcomes, optimising treatment plans, and enhancing clinical decision-making are three areas where machine learning (ML) models have grown in prominence in healthcare. But even though these models are very accurate predictors, they are frequently “black boxes,” which makes it hard for doctors to comprehend and rely on their results. For predictive healthcare models to be useful, doctors must be able to understand how the models work and why they are accurate. Only then can they improve patient care. This research seeks to delve into the intricate relationship between healthcare machine learning models' precision and interpretability, with an emphasis on creating methods that are both exceptionally precise and easily comprehensible for doctors. We look at various ML methods-like logistic regression, decision trees, and random forests-that make patient outcome predictions in a more understandable way, and we compare them to complicated models like deep learning, which focus on accuracy but aren't always transparent. To provide light on the decision-making processes of models without sacrificing predictive power, we investigate the potential applications of models such as explainable AI (XAI) and feature significance approaches to clinical datasets. The “sweet spot” where an improved model's predictability remains unaffected by efforts to make it more explainable is an important part of this work.