Traditional and Novel Predictive Models of the Heart Disease

Jiayi Liang

2025

Abstract

Considering that the leading cause of mortality worldwide is still cardiovascular disease, prompt risk assessment might facilitate proactive treatment strategies that enhance patient outcomes and lessen the financial burden on healthcare systems. Predicting heart disease is therefore critical to reducing mortality, reducing complications, and improving patient outcomes through early intervention. Predictive algorithms can identify high-risk patients before symptoms appear, but traditional diagnostic techniques frequently miss early-stage disease. Machine learning techniques like K-nearest Neighbors (KNN) and Decision Trees (DT) have become more and more popular for the prediction of cardiac illness. The interpretability, accuracy, and computing efficiency of these models varies. Prediction accuracy has been further enhanced by recent developments like Weighted Associative Rule Mining (WARM) and ensemble learning approaches (XBoost, Adaboost, and random subspace classifiers). These approaches still have issues with generalization, overfitting, interpretability of the model, and computational complexity. In order to produce more precise, individualized, and interpretable forecasts, future advancements in cardiac disease prediction are probably going to concentrate on hybrid models, explainable artificial intelligence (XAI), and multimodal data integration. With the goal of improving heart disease risk assessment with AI-driven healthcare solutions, this paper examines both conventional and innovative predictive models, their limitations, and potential future paths.

Paper Citation

in Harvard Style

Liang J. (2025). Traditional and Novel Predictive Models of the Heart Disease. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 355-359. DOI: 10.5220/0013690400004670

in Bibtex Style

@conference{icdse25,
author={Jiayi Liang},
title={Traditional and Novel Predictive Models of the Heart Disease},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={355-359},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013690400004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Traditional and Novel Predictive Models of the Heart Disease
SN - 978-989-758-765-8
AU - Liang J.
PY - 2025
SP - 355
EP - 359
DO - 10.5220/0013690400004670
PB - SciTePress