Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy
Abstract Background Machine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML alg...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-09-01
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| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-023-03520-4 |
| _version_ | 1797578352311140352 |
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| author | Mei Zhou Yongjian Deng Yi Liu Xiaolin Su Xiaocong Zeng |
| author_facet | Mei Zhou Yongjian Deng Yi Liu Xiaolin Su Xiaocong Zeng |
| author_sort | Mei Zhou |
| collection | DOAJ |
| description | Abstract Background Machine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML algorithm that combines echocardiographic data to automatically differentiate ischemic cardiomyopathy (ICM) from dilated cardiomyopathy (DCM). Methods We retrospectively collected the echocardiographic data of 200 DCM patients and 199 ICM patients treated in the First Affiliated Hospital of Guangxi Medical University between July 2016 and March 2022. All patients underwent invasive coronary angiography for diagnosis of ICM or DCM. The data were randomly divided into a training set and a test set via 10-fold cross-validation. Four ML algorithms (random forest, logistic regression, neural network, and XGBoost [ML algorithm under gradient boosting framework]) were used to generate a training model for the optimal subset, and the parameters were optimized. Finally, model performance was independently evaluated on the test set, and external validation was performed on 79 patients from another center. Results Compared with the logistic regression model (area under the curve [AUC] = 0.925), neural network model (AUC = 0.893), and random forest model (AUC = 0.900), the XGBoost model had the best identification rate, with an average sensitivity of 72% and average specificity of 78%. The average accuracy was 75%, and the AUC of the optimal subset was 0.934. External validation produced an AUC of 0.804, accuracy of 78%, sensitivity of 64% and specificity of 93%. Conclusions We demonstrate that utilizing advanced ML algorithms can help to differentiate ICM from DCM and provide appreciable precision for etiological diagnosis and individualized treatment of heart failure patients. |
| first_indexed | 2024-03-10T22:20:44Z |
| format | Article |
| id | doaj.art-db722a9c4eb5476994c17d26baf8985d |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-03-10T22:20:44Z |
| publishDate | 2023-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-db722a9c4eb5476994c17d26baf8985d2023-11-19T12:18:13ZengBMCBMC Cardiovascular Disorders1471-22612023-09-0123111010.1186/s12872-023-03520-4Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathyMei Zhou0Yongjian Deng1Yi Liu2Xiaolin Su3Xiaocong Zeng4Department of Cardiology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Cardiology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Cardiology, The First Affiliated Hospital of Guangxi Medical UniversityDepartment of Cardiology, Minzu Hospital of Guangxi Zhuang Autonomous RegionDepartment of Cardiology, The First Affiliated Hospital of Guangxi Medical UniversityAbstract Background Machine learning (ML) can identify and integrate connections among data and has the potential to predict events. Heart failure is primarily caused by cardiomyopathy, and different etiologies require different treatments. The present study examined the diagnostic value of a ML algorithm that combines echocardiographic data to automatically differentiate ischemic cardiomyopathy (ICM) from dilated cardiomyopathy (DCM). Methods We retrospectively collected the echocardiographic data of 200 DCM patients and 199 ICM patients treated in the First Affiliated Hospital of Guangxi Medical University between July 2016 and March 2022. All patients underwent invasive coronary angiography for diagnosis of ICM or DCM. The data were randomly divided into a training set and a test set via 10-fold cross-validation. Four ML algorithms (random forest, logistic regression, neural network, and XGBoost [ML algorithm under gradient boosting framework]) were used to generate a training model for the optimal subset, and the parameters were optimized. Finally, model performance was independently evaluated on the test set, and external validation was performed on 79 patients from another center. Results Compared with the logistic regression model (area under the curve [AUC] = 0.925), neural network model (AUC = 0.893), and random forest model (AUC = 0.900), the XGBoost model had the best identification rate, with an average sensitivity of 72% and average specificity of 78%. The average accuracy was 75%, and the AUC of the optimal subset was 0.934. External validation produced an AUC of 0.804, accuracy of 78%, sensitivity of 64% and specificity of 93%. Conclusions We demonstrate that utilizing advanced ML algorithms can help to differentiate ICM from DCM and provide appreciable precision for etiological diagnosis and individualized treatment of heart failure patients.https://doi.org/10.1186/s12872-023-03520-4Machine learningHeart failureIschemic cardiomyopathyDilated cardiomyopathyEchocardiography |
| spellingShingle | Mei Zhou Yongjian Deng Yi Liu Xiaolin Su Xiaocong Zeng Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy BMC Cardiovascular Disorders Machine learning Heart failure Ischemic cardiomyopathy Dilated cardiomyopathy Echocardiography |
| title | Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy |
| title_full | Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy |
| title_fullStr | Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy |
| title_full_unstemmed | Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy |
| title_short | Echocardiography-based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy |
| title_sort | echocardiography based machine learning algorithm for distinguishing ischemic cardiomyopathy from dilated cardiomyopathy |
| topic | Machine learning Heart failure Ischemic cardiomyopathy Dilated cardiomyopathy Echocardiography |
| url | https://doi.org/10.1186/s12872-023-03520-4 |
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