An Accurate Assessment of Cardiovascular Disorders Utilizing a Hybrid Random Forest Approach

K. Riddhi, T. Venkata Sai Kushvanth Reddy, P. Rahul, P. Kenny Adams, G. Mary Swarnalatha

2025

Abstract

According to recent studies, one of the leading causes of death worldwide is heart disease. Therefore, its accurate representation and early prediction is vital from a health care point of view. Studies have shown that machine learning techniques have performed well in heart disease predictions using patient data. As part of this effort, a machine learning-based predictive model for heart disease is developed, with a particular emphasis on the Random Forest method. The model is based on a dataset containing various health parameters of the patients such as age, cholesterol, blood pressure and other relevant medical components. Utilizing Random Forest ensemble learning, the model achieves optimum accuracy, high robustness and ease of interpretability. Accuracy, precision, F1 score, and recall were among the measures used to estimate the model's final reading. Results confirmed that effectiveness of the Random Forest classifier in predicting heart disease and proved to be beneficial for health practitioners with regards to early diagnosis and risk assessment.

Paper Citation

in Harvard Style

Riddhi K., Reddy T., Rahul P., Adams P. and Swarnalatha G. (2025). An Accurate Assessment of Cardiovascular Disorders Utilizing a Hybrid Random Forest Approach. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 237-245. DOI: 10.5220/0013896000004919

in Bibtex Style

@conference{icrdicct`2525,
author={K. Riddhi and T. Reddy and P. Rahul and P. Adams and G. Swarnalatha},
title={An Accurate Assessment of Cardiovascular Disorders Utilizing a Hybrid Random Forest Approach},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={237-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013896000004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - An Accurate Assessment of Cardiovascular Disorders Utilizing a Hybrid Random Forest Approach
SN - 978-989-758-777-1
AU - Riddhi K.
AU - Reddy T.
AU - Rahul P.
AU - Adams P.
AU - Swarnalatha G.
PY - 2025
SP - 237
EP - 245
DO - 10.5220/0013896000004919
PB - SciTePress