Disease Prediction Using Ensemble Learning Models: A Comprehensive Approach

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]27/09/2025 at 1:59 PM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 26 33

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    Altaf O. Mulani, Swapnil R. Takale,

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  1. Professor, Assistant Professor, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Department of Electronics & Telecommunication Engineering, SKN Sinhgad College of Engineering, Pandharpur, Maharashtra, Maharashtra, India, India

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Abstract

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nIn recent years, ensemble learning techniques have become pivotal in advancing predictive analytics within healthcare, particularly for early disease detection. The inherent variability and complexity of medical data, often characterized by high dimensionality, class imbalance, and noise, make it challenging for standalone classifiers to maintain high predictive accuracy. Ensemble learning, by integrating multiple models through bagging, boosting, or stacking, offers a more robust and generalizable approach. This study explores the practical application of ensemble learning algorithms to predict diseases such as diabetes, cancer, and cardiovascular conditions using diverse, publicly available datasets. We employed data preprocessing techniques including normalization, imputation, and encoding to prepare clinical datasets. Recursive Feature Elimination (RFE) was applied to identify the most significant features and improve the quality of model inputs. Three ensemble learning methods: Random Forest, Gradient Boosting, and a meta-model-based Stacking Ensemble, were then trained and evaluated. Their performance was evaluated using metrics such as accuracy, precision, recall, and F1-score through multi-fold cross-validation. The results demonstrate that ensemble methods surpass individual models, with the stacking ensemble achieving 90% accuracy and delivering strong results across all evaluation criteria. Furthermore, we discuss deployment challenges such as model interpretability, training time, and integration with clinical decision support systems. In conclusion, ensemble learning models offer a scalable and effective pathway for building intelligent, data-driven diagnostic systems, capable of assisting healthcare professionals in making early and reliable disease predictions.nn

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Keywords: Ensemble learning, disease prediction, machine learning, medical diagnosis, classification models

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Communication Engineering & Systems ]

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How to cite this article:
nAltaf O. Mulani, Swapnil R. Takale. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Disease Prediction Using Ensemble Learning Models: A Comprehensive Approach[/if 2584]. Journal of Communication Engineering & Systems. 22/09/2025; 15(03):26-33.

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nAltaf O. Mulani, Swapnil R. Takale. [if 2584 equals=”][226 striphtml=1][else]Disease Prediction Using Ensemble Learning Models: A Comprehensive Approach[/if 2584]. Journal of Communication Engineering & Systems. 22/09/2025; 15(03):26-33. Available from: https://journals.stmjournals.com/joces/article=22/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 11/07/2025
Accepted 04/09/2025
Published 22/09/2025
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Publication Time 73 Days

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