PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

İsmail Burak YAVRU; Sevcan YILMAZ GÜNDÜZ

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

Makale detayları

PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Yazarlar:

İsmail Burak YAVRU | Sevcan YILMAZ GÜNDÜZ

Yayıncı:

Eskisehir Technical University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2022 Vol.23 Issue 2, pp.184-194

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000416821

pISSN:

2667-4211

DOI:

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

Özet:

 

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.

 


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. 

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Makale detayları

PREDICTING MYOCARDIAL INFARCTION COMPLICATIONS AND OUTCOMES WITH DEEP LEARNING

Yazarlar: İsmail Burak YAVRU | Sevcan YILMAZ GÜNDÜZ

Yayıncı: Eskisehir Technical University

Ana Başlıklar: Uygulamalı fizik

Dergi: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2022 Vol.23 Issue 2, pp.184-194

Belge Tipi: Makale

Yayın Tarihi: 6/2022

Belge Dili: İngilizce

Document ID (Org No.): 2000416821

pISSN: 2667-4211

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

Sayı

Yazarlar:

İsmail Burak YAVRU | Sevcan YILMAZ GÜNDÜZ

Yayıncı:

Eskisehir Technical University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2022 Vol.23 Issue 2, pp.184-194

Belge Tipi:

Makale

Yayın Tarihi:

6/2022

Belge Dili:

İngilizce

Document ID (Org No.):

2000416821

pISSN:

2667-4211

DOI:

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