Associations between body factors and heart disease using logistic regression and machine learning

Shu Pang

doi:10.1117/12.2669971

24 March 2023 Associations between body factors and heart disease using logistic regression and machine learning

Shu Pang

Proceedings Volume 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022); 126115I (2023) https://doi.org/10.1117/12.2669971
Event: International Conference on Biological Engineering and Medical Science (ICBioMed2022), 2022, Oxford, United Kingdom

Abstract

Cardiovascular diseases (CVDs) appear frequently all over the world, causing people to suffer from pain and death. These diseases can cause some lesions to other organs, and systems in the body will be impacted. This study aims at ensuring and verifying the etiology of CVDs, and predicting whether people may get heart diseases. The sample in this study is a data set that combines clinical data of four locations. Analyzed by logistic regression, conditional inference tree, and support vector machine, the results of the goodness of fit and prediction are both satisfactory. This regression model has verified that gender, age, type of chest pain, blood pressure, angina, Old-peak, and ST Slope of ECG all have a positive relationship with having CVDs. With the values of these variables rising, the probability of having diseases increases. The accuracy rate of prediction by conditional inference tree and support vector machine is also over 87% and 84%, respectively, indicating that these two models are instructive and helpful in CVD screening. The findings in this study help people be aware of the presence of pathogenic factors, be alert earlier, and have a clearer understanding of what is going on with their bodies.

Citation Download Citation

Shu Pang "Associations between body factors and heart disease using logistic regression and machine learning", Proc. SPIE 12611, Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022), 126115I (24 March 2023); https://doi.org/10.1117/12.2669971

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