A Novel Probabilistic Artificial Neural Networks Approach for Diagnosing Heart Disease

Sa'di, Sadri; Hashemi, Ramin; Abdollapour, Arman; Chalabi, Kamal; Salamat, Mohammad Amin

doi:10.5121/ijfcst.2015.5605

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…PNN model has a parallel structure and is special for information classification. In contrast to other ANNs such as MLP, PNN has a higher speed in training the data, and it finds answers faster than MLP [6,20,21]. This model consists of 3 layers: input layer, hidden layer, and output (competitive) layer.…”

Section: Proposed Modelmentioning

confidence: 99%

“…In fact, ANN adopts (adjust) its weights in response to the inputs at the training phase, such that the real output of ANN converges to the desired output. Once actual output of ANN becomes the desired output, the process of training the networks terminates, such that ANN leads to the least error rate [5,6,8,20,21]. After training the ANN using training data until achieving minimum error rate, other data which have no effects in training process feeds to the ANN as testing inputs.…”

Section: Proposed Modelmentioning

confidence: 99%

“…Finally, this layer selects probability values from probabilities vector and generates value 1 for it and 0 for other probabilities [6,20,21]. Gaussian transmission function which is used in hidden layer calculated as follows [20,21] Where P (P1, P2,…, Pn) is the input vector which consists n variables.…”

Section: Proposed Modelmentioning

confidence: 99%

“…This way, the patient can prevent irreversible complications and save his health. In addition, in recent years ANNs applied in all fields of medical sciences, and have been accepted by physician for both identifying the diseases and patient treatments [6,7]. When using ANNs for identifying diseases, the aim is to achieve a high accuracy rate, and quality of diagnosis depends on training and testing a data set [7].…”

Section: Introductionmentioning

confidence: 99%

“…Generally, diagnosis is a complex task which requires high skill and experience. Timely diagnosis and specialized medical care of patients can reduce issues of patients, as well as treatment costs in other therapeutic courses [5,6]. A lot of diverse solutions have been proposed for different diseases up to now.…”

Section: Introductionmentioning

confidence: 99%

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A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II

Soltani¹,

Jafarian²

2016

ijacsa

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Abstract-Diabetes is one of the major health problems as it causes physical disability and even death in people. Therefore, to diagnose this dangerous disease better, methods with minimum error rate must be used. Different models of artificial neural networks have the capability to diagnose this disease with minimum error. Hence, in this paper we have used probabilistic artificial neural networks for an approach to diagnose diabetes disease type II. We took advantage of Pima Indians Diabetes dataset with 768 samples in our experiments. According to this dataset, PNN is implemented in MATLAB. Furthermore, maximizing accuracy of diagnosing the Diabetes disease type II in training and testing the Pima Indians Diabetes dataset is the performance measure in this paper. Finally, we concluded that training accuracy and testing accuracy of the proposed method is 89.56% and 81.49%, respectively.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II

Soltani¹,

Jafarian²

2016

ijacsa

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Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

et al. 2017

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The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.

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Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks

et al. 2020

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Electrocardiograms (ECGs) play a vital role in the clinical diagnosis of heart diseases. An ECG record of the heart signal over time can be used to discover numerous arrhythmias. Our work is based on 15 different classes from the MIT-BIH arrhythmia dataset. But the MIT-BIH dataset is strongly imbalanced, which impairs the accuracy of deep learning models. We propose a novel data-augmentation technique using generative adversarial networks (GANs) to restore the balance of the dataset. Two deep learning approaches-an end-to-end approach and a two-stage hierarchical approach-based on deep convolutional neural networks (CNNs) are used to eliminate hand-engineering features by combining feature extraction, feature reduction, and classification into a single learning method. Results show that augmenting the original imbalanced dataset with generated heartbeats by using the proposed techniques more effectively improves the performance of ECG classification than using the same techniques trained only with the original dataset. Furthermore, we demonstrate that augmenting the heartbeats using GANs outperforms other common data augmentation techniques. Our experiments with these techniques achieved overall accuracy above 98.0%, precision above 90.0%, specificity above 97.4%, and sensitivity above 97.7% after the dataset had been balanced using GANs, results that outperform several other ECG classification methods. INDEX TERMS Class imbalance, convolution neural networks (CNNs), ECG classification, generative adversarial networks (GANs).

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A Novel Probabilistic Artificial Neural Networks Approach for Diagnosing Heart Disease

Cited by 9 publications

References 9 publications

A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II

A New Artificial Neural Networks Approach for Diagnosing Diabetes Disease Type II

Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks

Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks

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