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Robust cardiovascular disease prediction using logistic regression
Datta, Snigdha Gang, Isaac K.
Datta, Snigdha
Gang, Isaac K.
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Authors
Datta, Snigdha
Gang, Isaac K.
Gang, Isaac K.
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Original Date
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Issue Date
2021-06
Type
Article
Genre
Keywords
Machine learning,Data mining,Artificial intelligence,Cardiovascular diseases,Laboratory tests,Robustness (mathematics),Statistical models
Subjects (LCSH)
Citation
Datta, S., & Gang, I. K. (2021). Robust cardiovascular disease prediction using logistic regression. Journal of Management & Engineering Integration, 14(1), 26-36. https://doi.org/10.62704/10057/24766
Abstract
Cardiovascular disease, commonly known as heart disease, is one of the leading causes of death in the United States and worldwide. Early detection of the disease can save thousands of lives and billions of dollars in healthcare costs. A statistical model with the ability to accurately predict heart disease could be of immense help to the patients, their families, the medical community, and the healthcare system. Hospitals and providers collect many patient health metrics during screening and routine lab tests, which could be used to build such a statistical model. A robust heart disease prediction model is built using a sample dataset from the University of California, Irvine Machine Learning repository. Initial Hypotheses are formulated, and the most significant predictor variables are identified using the Wald test. The statistical significance of the proposed model is tested using the Likelihood-Ratio test. A repeated 10-fold cross-validation technique is used to evaluate the model's prediction power on previously unseen data. Keeping in mind the simplicity, usability, and explainability of results to the medical community, a Logistic Regression model that predicts the heart disease class with a high degree of accuracy is presented in this paper.
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Description
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, December 2022.
Publisher
Association for Industry, Engineering and Management Systems (AIEMS)
Journal
Book Title
Series
Journal of Management & Engineering Integration
v.14 no.1
v.14 no.1
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Archival Collection
PubMed ID
ISSN
1939-7984