An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

Ali, Syed Arslan; Raza, Basit; Malik, Ahmad Kamran; Faheem, Muhammad; Alquhayz, Hani; Kumar, Yogan Jaya

doi:10.1109/access.2020.2985646

Cited by 58 publications

(22 citation statements)

References 33 publications

(46 reference statements)

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“…Overall, the proposed model has following advantages compared with the state-of-the-art methods [56][57][58][59] :…”

Section: Discussionmentioning

confidence: 99%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang

2021

BMC Med Inform Decis Mak

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Background Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

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“…Overall, the proposed model has following advantages compared with the state-of-the-art methods [56][57][58][59] :…”

Section: Discussionmentioning

confidence: 99%

A hybrid cost-sensitive ensemble for heart disease prediction

Zhang

2021

BMC Med Inform Decis Mak

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“…An improved Recurrent Neural Network (RNN) model of deep learning was proposed by Krishnan et al [ 23 ] for improving the accuracy of HD prediction. The existence of numerous Gated Recurrent Units (GRU) has improved the performance of the RNN model, which now has an accuracy rate of 98.4% and a processing time that is much faster.…”

Section: Related Workmentioning

confidence: 99%

A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models

et al. 2022

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Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.

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“…For this study, we utilized the Cleveland dataset obtained from the machine learning repository at the University of California, Irvine (UCI) [23]. This dataset comprises 76 raw attributes, but only a subgroup of 13 features is mostly used in research for the prediction of heart disease [24]. The 13 attributes include age, gender, type of chest pain, blood pressure, cholesterol level, maximum heart rate, fasting blood sugar, exerciseinduced angina, resting ECG, ST depression, ST slope, thalassemia, number of significant vessels colored by fluoroscopy.…”

Section: Heart Disease Datasetmentioning

confidence: 99%

Enhanced accuracy for heart disease prediction using artificial neural network

Sarra

Dinar

Mohammed

2022

IJEECS

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Making an accurate and timely diagnosis of cardiac disease is critical for preventing and treating heart failure. The accuracy of results produced by traditional machine learning (ML) algorithms is satisfactory. On the other hand, deep learning algorithms result in higher prediction accuracy. In this study, we used an artificial neural network (ANN) model to construct a deep learning diagnosis system for heart disease prediction. The developed ANN prediction model achieved 93.44% accuracy, which is 7.5% higher than a traditional ML model support vector machine (SVM). Additionally, using a simpler neural network reduced the time taken for training and classification to less than a minute.

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An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm

Cited by 58 publications

References 33 publications

A hybrid cost-sensitive ensemble for heart disease prediction

A hybrid cost-sensitive ensemble for heart disease prediction

A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models

Enhanced accuracy for heart disease prediction using artificial neural network

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