Heart disease identification based on boosted decision tree

TANG Jiayao; YU Su

doi:10.12299/jsues.24-0219

Volume 38 Issue 4

Dec. 2024

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Article Navigation > Journal of Shanghai University of Engineering Science > 2024 > 38(4): 465-470

TANG Jiayao, YU Su. Heart disease identification based on boosted decision tree[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 465-470. doi: 10.12299/jsues.24-0219

Citation:

TANG Jiayao, YU Su. Heart disease identification based on boosted decision tree[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 465-470. doi: 10.12299/jsues.24-0219

TANG Jiayao, YU Su. Heart disease identification based on boosted decision tree[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 465-470. doi: 10.12299/jsues.24-0219

Citation:

TANG Jiayao, YU Su. Heart disease identification based on boosted decision tree[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 465-470. doi: 10.12299/jsues.24-0219

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Heart disease identification based on boosted decision tree

doi: 10.12299/jsues.24-0219

TANG Jiayao,
YU Su^,

School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2024-06-06
Publish Date: 2024-12-31

Abstract

Abstract

A gradient boosting decision tree (BDTG) model based on high-energy physics data analysis in ROOT framework was proposed for the identification of heart disease using a multivariate analysis method. Through a large amount of clinical data, the aim is to analyze the various complex relationships of variables to improve the accuracy and reliability of heart disease differentiation. Using the Kaggle open-source heart disease dataset, the results showed that the model did not exhibit any erroneous discrimination when the BDTG responsevalues range between between −0.4 and 0.5. In addition, when the truncation of BDTG response values is −0.6 or 0.6, the model still maintained above 98% in accuracy, precision, recall and F1 scores. Therefore, the model has high accuracy and reliability in the diagnosis of heart disease. This study not only provides new perspectives and methods for predicting heart disease, but also serves as a reference for machine learning prediction research on other diseases.
- heart disease prediction,
- machine learning,
- gradient boosting decision tree (BDTG) algorithm

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References(18)

References

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