J. Cruz Antony; E. Murali; D. Deepa; R. Vignesh; S. Hemalatha; Umme Fahad

Data Transformation and Predictive Analytics of Cardiovascular Disease Using Machine and Ensemble Learning Techniques

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Author(s)

J. Cruz Antony ^1,* E. Murali ¹ D. Deepa ¹ R. Vignesh ¹ S. Hemalatha ¹ Umme Fahad ²

1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India

2. Astrophysics Research Institute, Liverpool John Moores University, Liverpool, UK

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2025.01.06

Received: 5 Jul. 2024 / Revised: 19 Sep. 2024 / Accepted: 1 Nov. 2024 / Published: 8 Feb. 2025

Index Terms

Cardiovascular, Data Transformation, Ensemble Learning, Heart Disease, Machine Learning

Abstract

About one person dies every minute from cardiovascular disease; consequently, it has almost surpassed war as the largest cause of death in the twenty-first century. In cardiology, early and accurate diagnosis of heart illness is a cornerstone of effective healthcare. Predictive analytics, which involves machine-learning algorithms, can be a great option for contributing towards the early detection of cardiovascular disease. This study evaluates the data preprocessing techniques involved in building machine learning models to predict cardiovascular disease and identify the features contributing to the cardio attack. A novel data transformation technique named the superlative boundary binning method was proposed to enhance machine learning and ensemble learning classification models for predicting cardiac illness based on independent physiological feature parameters. The results revealed that the ensemble learning classifier AdaBoost using the superlative boundary binning method has performed well with a classification accuracy of 93% when compared with the other data transformation and machine learning classifier models.

Cite This Paper

J. Cruz Antony, E. Murali, D. Deepa, R. Vignesh, S. Hemalatha, Umme Fahad, "Data Transformation and Predictive Analytics of Cardiovascular Disease Using Machine and Ensemble Learning Techniques", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.1, pp.88-97, 2025. DOI:10.5815/ijisa.2025.01.06

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