Logistic Regression in Healthcare: Predictive Modeling for Enhanced Clinical Decision-Making

Authors

  • Prachi Saoji
  • Lakshmi Madireddy
  • Ajeet Saoji

Keywords:

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Abstract

In clinical practice, logistic regression is becoming a key tool that allows health care practitioners to forecast binary outcomes associated with a wide range of diseases.

To identify risk factors and assist clinical decision making, this review aims to lime-light the significance of various models by looking at important factors including lifestyle, clinical conditions, and socio-demographic data.

Logistic regression has proven advantages like, interpretation and adaptation to different datasets, while it also has drawbacks like, it presumes that predictors and outcomes have linear correlations. Carefully validating models and knowledge of the context in which these models are used are mandatory to overcome these problems.

In order to overcome these constraints, the combination of logistic regression along with machine learning techniques shows promise in enhancing the clinical decision making. This review emphasizes the positive effect of logistic regression on decision making in the rapidly growing healthcare industry.

 

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Published

2025-05-12

How to Cite

1.
Saoji P, Madireddy L, Saoji A. Logistic Regression in Healthcare: Predictive Modeling for Enhanced Clinical Decision-Making. J Neonatal Surg [Internet]. 2025May12 [cited 2025Oct.12];14(22S):765-9. Available from: https://jneonatalsurg.com/index.php/jns/article/view/5603

Issue

Vol. 14 No. 22S (2025): Journal of Neonatal Surgery

Section

Original Article

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