Ensemble Learning-Based Predictive Analysis for Heart Disease Detection

DOI

10.2991/978-94-6239-693-7_6How to use a DOI?

Keywords

Ensemble Machine Learning; Decision Trees; Random Forests; Bagging & Boosting; Accuracy; Healthcare Diagnostics

Abstract

Heart disease remains one of the leading causes of illness and mortality worldwide, making quick and accurate diagnosis more essential. Many doctors rely on multiple clinical indicators such as patient’s age, lifestyle habits, and laboratory test results to evaluate a patient’s risk. Most existing computerized prediction systems use a single machine learning model and rely on only few parameters, which will likely limit the reliability of predictions and will not be able to give a complete picture of a patient’s overall. The suggestion is to develop an ensemble machine learning method for predicting patient behavior. However, rather than relying on single predictive model, we use collective power of multiple different classifiers, which can be accomplished through a combination of bagging and boosting techniques. By evaluating population demo- graphic information, biochemistry laboratory test results, and lifestyle habits, this method minimizes both bias and overfitting which means the model provides more stable predictions. The performance of the model is measured with standard metrics, thus providing the opportunity for earlier detection, better clinical decisions and timely diagnosis and individualized treatment options and preventative care, which allow for enhanced long-term heart health with improved outcomes.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pasala Velangini Nishitha
AU  - Vutla Nischitha
AU  - Bandhakavi Nitya Sriya
AU  - K. Subba Shankar
PY  - 2026
DA  - 2026/06/16
TI  - Ensemble Learning-Based Predictive Analysis for Heart Disease Detection
BT  - Proceedings of the International Conference on Intelligent Systems for a Sustainable Future (ISSF 2026)
PB  - Atlantis Press
SP  - 42
EP  - 49
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-693-7_6
DO  - 10.2991/978-94-6239-693-7_6
ID  - Nishitha2026
ER  -