Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach

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Moradi Elahe

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING Year:2024 | Volume:20 | Issue:4 Page(s): 55-67

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Information Journal Paper

Title

Diagnosis of Coronary Heart Disease via Robust Artificial Neural Network Classifier by Adaptive Synthetic Sampling Approach

Author(s)

Moradi Elahe | Issue Writer Certificate

Keywords

Artificial neural network

Robust classifier

Imbalanced dataset

Adaptive synthetic sampling approach

Machine Learning

Abstract

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.

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