A Comparative Study of Supervised Learning Algorithms for Predictive Analytics in Healthcare

Author(s)	Saritha Putta
Country	India
Abstract	Predictive analytics has emerged as an important tool in modern health care, allowing for early identification and effective allocation of resources to improve health outcomes. Supervised machine learning algorithms are typically used to generate predictive models from historical health care data. This study gives an overview of commonly used supervised learning algorithms: Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Gradient Boosting and how these methods have been used in health care applications. With publicly available secondary data sets, including UCI Heart Disease and Diabetes data sets, we examined the perceived performance of the algorithms against standard performance measures: accuracy, precision, recall, F1 score and AUC-ROC. Our findings indicate that ensemble methods, such as Random Forest and Gradient Boosting tend to outperform traditional classifiers; however, the choice of which algorithm to use should reflect the importance and context of the health care task. This study discusses trade-offs between model complexity, interpretability, and predictive capabilities in a health care context, thus providing valuable context for selecting appropriate models for health care analytics.
Keywords	Predictive analysis, health care, supervised learning, SVM, Decision trees.
Field	Medical / Pharmacy
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-04

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