Optimizing Decision Tree Classifiers for Healthcare Predictions: A Comparative Analysis of Model Depth, Pruning, and Performance

Abstract

This study presents of Decision Tree classifiers for predictive modeling in medicine, focusing on model depth optimization, pruning techniques, and performance evaluation. On the basis of a synthetic healthcare dataset containing over 55,000 records, each with features such as age, gender, blood type, bill amount, and medical condition, we investigate the impact of varying tree depth from 1 to 5 on predictive accuracy, interpretability, and generalizability. Shallow models have strong transparency but poor classification strength, and deep models obtain stronger interactions but suffer from overfitting. With pruning, we find a balance between model simplicity and precision and yield strong classifiers for practice. With comparative examination through confusion matrices, feature importance graphs, and accuracy measures, the research presents thorough details about how Decision Tree configurations affect healthcare prediction tasks. The findings emphasize the need for interpretable artificial intelligence (XAI) methods in medical machine learning, stressing the attractiveness of models that find a balance between interpretability and sound performance. The research contributes a pragmatic approach to model selection and optimization in medical analytics that supports the creation of decision-support systems that are clinically valid, ethical, and regulatory compliant.