Predictive Modeling for Healthcare: Leveraging Categorical and Binary Data for Enhanced Accuracy

Such dramatic growth in healthcare data has increased the necessity of developing predictive models capable of manipulating and processing categorical and binary data, which would constitute significant improvements in the predictability of diseases and patient management strategies. Traditional statistical approaches such as Logistic Regression have been utilized frequently but often fail when managing these complex relationships inherent to large health care datasets. Several studies recently shown that, in comparison with traditional methods, advanced machine learning techniques such as Random Forest, CatBoost, and Gradient Boosting can better capture complex, non-linear patterns and data interactions toward a high level of predictive accuracy. This paper systematically applies and evaluates these new techniques on healthcare datasets while focusing on the optimization of main performance metrics: accuracy, sensitivity, and specificity. Experimental results demonstrate that ensemble models, especially CatBoost, outperform traditional models in terms of prediction accuracy, and thus can be adapted to robust solutions in patient risk classification and early detection of diseases. The findings imply the importance of advanced machine learning methods in supporting the personalization of treatment plans and allocation of resources, leading towards exact, data-driven decision-making in healthcare systems.