An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

Waqar, Muhammad; Dawood, Hassan; Dawood, Hussain; Majeed, Muhammad Nadeem; Banjar, Ameen; Alharbey, Riad

doi:10.1155/2021/6621622

Cited by 49 publications

(23 citation statements)

References 22 publications

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“…The results obtained the highest accuracy rate (96.49%, 95.64%, 93.38%) in the multiclass hyperspectral image dataset when smote balanced with 5-fold crossvalidation was applied. Waqar et al (2021) proposed the method of SMOTE technique depended on a deep learning algorithm for the prediction of a heart attack. SMOTE is used to handle imbalanced datasets.…”

Section: Smote Approachmentioning

confidence: 99%

Combining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass Imbalanced

Panthong¹

2022

Journal of Computer Science

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The classification of real-world problems always consists of imbalanced and multiclass datasets. A dataset having unbalanced and multiple classes will have an impact on the pattern of the classification model and the classification accuracy, which will be decreased. Hence, oversampling method keeps the class of dataset balanced and avoids the overfitting problem. The purposes of the study were to handle multiclass imbalanced datasets and to improve the effectiveness of the classification model. This study proposed a hybrid method by combining the Synthetic Minority Oversampling Technique (SMOTE) and One-Versus-All (OVA) with deep learning and ensemble classifiers; stacking and random forest algorithms for multiclass imbalanced data handling. Datasets consisting of different numbers of classes and imbalances are gained from the UCI Machine Learning Repository. The research outputs illustrated that the presented method acquired the best accuracy value at 98.51% when the deep learning classifier was used to evaluate model classification performance in the new-thyroid dataset. The proposed method using the stacking algorithm received a higher accuracy rate than other methods in the car, pageblocks, and Ecoli datasets. In addition, the outputs gained the highest performance of classification at 98.47% in the dermatology dataset where the random forest is used as a classifier.

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Section: Smote Approachmentioning

confidence: 99%

Combining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass Imbalanced

Panthong¹

2022

Journal of Computer Science

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“…This is compounded by the fact that the presented dataset contains a skewed distribution of positive and negative classifications. In the end, this resulted in a cost-effective solution since feature engineering is regularly a time-eating endeavor [19]. Automating the risk prediction with the use of a binary classifier will save the doctor time and effort.…”

Section: Iirelated Workmentioning

confidence: 99%

An Automated Heart Disease Diagnosis System Using Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks

Kumar

Singh

2022

Preprint

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Heart disease is the devastating illness that kills the most people worldwide when compared to other ailments. Heart disease is a significant hazard. A cardiac problem occurs when the arteries supplying oxygen and blood to the heart become fully clogged or constricted. One of the primary causes of death has been heart disease. In a short period, the mortality rate has grown considerably. Because an incorrect diagnosis of the condition might result in death, accuracy and safe diagnosis of cardiac disease should be a top priority in healthcare. Cardiovascular disease may be avoided if the prognosis is right, but it can potentially be fatal if the prediction is incorrect. Hence, in this paper, we propose an automated heart disease diagnosis system. Initially, the patient data is collected and preprocessed. The features are extracted using Kernel-based Linear Discriminant Analysis (K-LDA). (Correlation-based feature selection) CFS is performed to extract relevant features. We propose Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks (ACLS-RCNN) for the prediction of the disease. We also introduce the Improved Cuttlefish-Swarm Optimization Algorithm (ICSOA) for enhancing the prediction process. The performance metrics like accuracy, precision, recall, and f1-score of the proposed system are evaluated using the python simulation tool and compared with conventional methodologies. Our framework can make a very accurate prediction based on clinical data and diagnoses from the real world.

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“…The author used SMOTE technique to balance the dataset without feature selection. The balanced dataset was trained and tested by a deep neural network to predict the absence and presence of a heart attack and obtained 96% accuracy [17]. Recently, Ishaq et al used SMOTE to balance data distribution and extremely randomized trees (ET) on selected parameters to predict patient survival using RF importance ranking [18].…”

Section: Introductionmentioning

confidence: 99%

An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods

et al. 2022

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Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient's severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient's current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy.

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An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction

Cited by 49 publications

References 22 publications

Combining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass Imbalanced

Combining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass Imbalanced

An Automated Heart Disease Diagnosis System Using Adaptive Cross-Layer Stacked Residual Convolutional Neural Networks

An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods

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