PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

İsmail Burak Yavru; Sevcan Yılmaz Gündüz

doi:10.18038/estubtda.1056821

EN TR

PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Abstract

Early diagnosis of cardiovascular diseases, which have high mortality rates all over the world, can save many lives. Various clinical findings and past histories of patients play an important role in diagnosing these diseases. These days, the prediction of cardiovascular diseases has gained great importance in the medical field. Pathological studies are prone to misinterpretation because too many findings are studied. For this reason, many automatic models that work with machine learning methods on patients' findings have been proposed. In this study, a model that predicts twelve myocardial infarction complications based on clinical findings is proposed. The proposed model is a deep learning model with three hidden layers with dropouts and a skip connection. A binary accuracy metric is used for measuring the performance of the proposed method. Rectified Linear Unit is set to the hidden layers and sigmoid function to the output layer as an activation function. Experiments were performed on a real dataset with 1700 patient records and carried out on two main scenarios; training on original data and training on augmented data with 100 epochs. As a result of the experiments, a total accuracy rate of 92% was achieved which is the best accuracy rate that has been proposed on this dataset.

Keywords

Deep Learning, Myocardial Infarction, Data Augmentation, Artificial Intelligence, Prediction

PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Abstract

Early diagnosis of cardiovascular diseases, which have high mortality rates all over the world, can save many lives. Various clinical findings and past histories of patients play an important role in diagnosing these diseases. These days, the prediction of cardiovascular diseases has gained great importance in the medical field. Pathological studies are prone to misinterpretation because too many findings are studied. For this reason, many automatic models that work with machine learning methods on patients' findings have been proposed. In this study, a model that predicts twelve myocardial infarction complications based on clinical findings is proposed. The proposed model is a deep learning model with three hidden layers with dropouts and a skip connection. A binary accuracy metric is used for measuring the performance of the proposed method. Rectified Linear Unit is set to the hidden layers and sigmoid function to the output layer as an activation function. Experiments were performed on a real dataset with 1700 patient records and carried out on two main scenarios; training on original data and training on augmented data with 100 epochs. As a result of the experiments, a total accuracy rate of 92% was achieved which is the best accuracy rate that has been proposed on this dataset.

Keywords

Deep Learning,, Myocardial Infarction, Data Augmentation, Artificial Intelligence,, Prediction

References

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

İsmail Burak Yavru ^*
0000-0002-7364-6166
Türkiye

Sevcan Yılmaz Gündüz
0000-0002-1736-9942
Türkiye

Publication Date

June 28, 2022

Submission Date

January 12, 2022

Acceptance Date

April 28, 2022

Published in Issue

Year 2022 Volume: 23 Number: 2

DOI

https://doi.org/10.18038/estubtda.1056821

IZ

https://izlik.org/JA65YE74TY

APA

Yavru, İ. B., & Yılmaz Gündüz, S. (2022). PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, 23(2), 184-194. https://doi.org/10.18038/estubtda.1056821

AMA

1.Yavru İB, Yılmaz Gündüz S. PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING. Estuscience - Se. 2022;23(2):184-194. doi:10.18038/estubtda.1056821

Chicago

Yavru, İsmail Burak, and Sevcan Yılmaz Gündüz. 2022. “PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 23 (2): 184-94. https://doi.org/10.18038/estubtda.1056821.

EndNote

Yavru İB, Yılmaz Gündüz S (June 1, 2022) PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 23 2 184–194.

IEEE

[1]İ. B. Yavru and S. Yılmaz Gündüz, “PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING”, Estuscience - Se, vol. 23, no. 2, pp. 184–194, June 2022, doi: 10.18038/estubtda.1056821.

ISNAD

Yavru, İsmail Burak - Yılmaz Gündüz, Sevcan. “PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 23/2 (June 1, 2022): 184-194. https://doi.org/10.18038/estubtda.1056821.

JAMA

1.Yavru İB, Yılmaz Gündüz S. PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING. Estuscience - Se. 2022;23:184–194.

MLA

Yavru, İsmail Burak, and Sevcan Yılmaz Gündüz. “PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING”. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering, vol. 23, no. 2, June 2022, pp. 184-9, doi:10.18038/estubtda.1056821.

Vancouver

1.İsmail Burak Yavru, Sevcan Yılmaz Gündüz. PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING. Estuscience - Se. 2022 Jun. 1;23(2):184-9. doi:10.18038/estubtda.1056821

PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Abstract

Keywords

PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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