Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach
With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robus...
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Iran University of Science and Technology
2024-11-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3384-en.pdf |
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| author | Elahe Moradi |
| author_facet | Elahe Moradi |
| author_sort | Elahe Moradi |
| collection | DOAJ |
| description | With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robust artificial neural network classifier (RANNC) technique for the prediction of CHD. The dataset CHD in this paper has imbalanced data, and in addition, it has some outlier values. The dataset consists of information related to 4240 samples with 16 attributes. Due to the presence of outliers, a robust method has been used to scale the dataset. On the other hand, due to the imbalance of CHD data, three data balancing methods, including Random Over Sampling (ROS), Synthetic Minority Over Sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) approaches, have been applied to the CHD data set. Also, six artificial intelligence algorithms, including LRC, DTC, RFC, KNNC, SVC, and ANN, have been evaluated on the considered dataset with criteria such as precision, accuracy, recall, F1-score, and MCC. The RANNC, leveraging ADASYN to address data imbalance and outliers, significantly improved CHD diagnostic accuracy and the reliability of healthcare predictive models. It outperformed other artificial intelligence methods, achieving precision, accuracy, recall, F1-score, and MCC scores of 95.57%, 96.90%, 99.70%, 97.59%, and 93.42%, respectively. |
| format | Article |
| id | doaj-art-ad31e40b3f6945fcbad64a7852ff2469 |
| institution | Kabale University |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-ad31e40b3f6945fcbad64a7852ff24692025-01-09T18:47:15ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902024-11-012045567Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling ApproachElahe Moradi0 Department of Electrical and Computer Engineering Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran With the intricate interplay between clinical and pathological data in coronary heart disease (CHD) diagnosis, there is a growing interest among researchers and healthcare providers in developing more accurate and reliable predictive methods. In this paper, we propose a new method entitled the robust artificial neural network classifier (RANNC) technique for the prediction of CHD. The dataset CHD in this paper has imbalanced data, and in addition, it has some outlier values. The dataset consists of information related to 4240 samples with 16 attributes. Due to the presence of outliers, a robust method has been used to scale the dataset. On the other hand, due to the imbalance of CHD data, three data balancing methods, including Random Over Sampling (ROS), Synthetic Minority Over Sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN) approaches, have been applied to the CHD data set. Also, six artificial intelligence algorithms, including LRC, DTC, RFC, KNNC, SVC, and ANN, have been evaluated on the considered dataset with criteria such as precision, accuracy, recall, F1-score, and MCC. The RANNC, leveraging ADASYN to address data imbalance and outliers, significantly improved CHD diagnostic accuracy and the reliability of healthcare predictive models. It outperformed other artificial intelligence methods, achieving precision, accuracy, recall, F1-score, and MCC scores of 95.57%, 96.90%, 99.70%, 97.59%, and 93.42%, respectively.http://ijeee.iust.ac.ir/article-1-3384-en.pdfartificial neural networkrobust classifierimbalanced datasetadaptive synthetic sampling approachmachine learning |
| spellingShingle | Elahe Moradi Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach Iranian Journal of Electrical and Electronic Engineering artificial neural network robust classifier imbalanced dataset adaptive synthetic sampling approach machine learning |
| title | Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach |
| title_full | Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach |
| title_fullStr | Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach |
| title_full_unstemmed | Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach |
| title_short | Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach |
| title_sort | diagnosis of coronary heart disease via robust artificial neural network classifier by adaptive synthetic sampling approach |
| topic | artificial neural network robust classifier imbalanced dataset adaptive synthetic sampling approach machine learning |
| url | http://ijeee.iust.ac.ir/article-1-3384-en.pdf |
| work_keys_str_mv | AT elahemoradi diagnosisofcoronaryheartdiseaseviarobustartificialneuralnetworkclassifierbyadaptivesyntheticsamplingapproach |