Predictive Assessment of Heart Disease Based on Multiple Machine Learning Models

Zhongyi Zhang

2024

Abstract

Cardiovascular disease has been one of the leading causes of many deaths, and early diagnosis can improve treatment outcomes and survival rates. In this paper, six mainstream algorithmic models applicable to dichotomization are compared to predict whether a person is suffering from heart disease based on a number of features, such as gender, age, type of chest pain, resting Electrocardiograph (ECG) results, and maximum heart rate. The study first explored the relationship between the values of these features and tried to analyze the main factors affecting heart disease among them, then the dataset was divided in a way that the test set was 30% and the training set was 70% to train the model, and finally the six algorithmic models were used to predict the dataset, and the training results showed that Support Vector Machine (SVM) algorithmic model could provide more accurate data for the prediction of heart disease. This paper provides new tools and ideas for clinical diagnosis, treatment and prevention in the field of cardiovascular medicine, and contributes to the improvement of patients' quality of life and the reduction of medical costs.

Paper Citation

in Harvard Style

Zhang Z. (2024). Predictive Assessment of Heart Disease Based on Multiple Machine Learning Models. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 323-327. DOI: 10.5220/0012937500004508

in Bibtex Style

@conference{emiti24,
author={Zhongyi Zhang},
title={Predictive Assessment of Heart Disease Based on Multiple Machine Learning Models},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={323-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012937500004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Predictive Assessment of Heart Disease Based on Multiple Machine Learning Models
SN - 978-989-758-713-9
AU - Zhang Z.
PY - 2024
SP - 323
EP - 327
DO - 10.5220/0012937500004508
PB - SciTePress