A Hybrid Machine Learning Approach for Enhanced Patient Diagnosis and Health Outcome Prediction

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Year : 2026 | Volume : 13 | Issue : 02 | Page :
By

Ekta,

Shruti Mathur,

Shreya agarwal,

  1. Research Scholar, Department of Computer Science & Engineering, JECRC University Jaipur, Rajasthan, India
  2. Professor, Department of Computer Science & Engineering, JECRC University Jaipur, Rajasthan, India
  3. Assistant professor, Department of Computer Science & Engineering, JECRC University Jaipur, Rajasthan, India

Abstract

Rapid and accurate diagnosis is essential to present day practitioners of medicine, yet can be complicated by the enormous volume and complexity inherent in clinical data. To this end, we here propose a hybrid machine learning model in combination with Recursive Feature Elimination (RFE) and ensemble voting to enhance the diagnostic accuracy by integrating multiple models. Trained on real-world electronic health data, including lab results, demographics and medical history, the model excels across a range of key metrics like accuracy, recall and AUC. The data in trials suggest how well it predicts chronic diseases, those that take years or decades to cause symptoms like diabetes and heart problems. The strategy we proposed demonstrates the value of smart diagnostic tools to enhance patient care and clinical diagnosis.

Keywords: Health Outcome, Ensemble Learning, Feature Selection, Predictive Analytics, medical history

[This article belongs to Research & Reviews: A Journal of Bioinformatics ]

How to cite this article: Ekta, Shruti Mathur, Shreya agarwal. A Hybrid Machine Learning Approach for Enhanced Patient Diagnosis and Health Outcome Prediction. Research & Reviews: A Journal of Bioinformatics. 2026; 13(02):-.
How to cite this URL: Ekta, Shruti Mathur, Shreya agarwal. A Hybrid Machine Learning Approach for Enhanced Patient Diagnosis and Health Outcome Prediction. Research & Reviews: A Journal of Bioinformatics. 2026; 13(02):-. Available from: https://journals.stmjournals.com/rrjobi/article=2026/view=253517

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Regular Issue Subscription Review Article
Volume 13
Issue 02
Received 05/02/2026
Accepted 23/06/2026
Published 27/08/2026
Publication Time 203 Days

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