Heart diseases classification using convolutional neural network

Gawande, Nikhil; Barhatte, Alka

doi:10.1109/cesys.2017.8321264

Cited by 26 publications

(5 citation statements)

References 8 publications

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“…The classification of the accuracy value is a presentation of the accuracy of the data record after testing the classification results [24]. The method used to calculate the accuracy value is formulated in (9).…”

Section: Discussionmentioning

confidence: 99%

“…Further researchers also classify images as malware using CNN, where the accuracy rate can reach 98% [8]. The research entitled "heart diseases classification using convolutional neural network" obtained an accuracy of 99.46% [9]. Additionally, research in implementing the CNN method to detect the use of masks has an accuracy rate of more than 96% [10], while the classification of 10 CNN-based sports activities obtains an accuracy rate of 99.30% [11].…”

Section: Introductionmentioning

confidence: 99%

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Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm

Anggoro,

Marzuki,

Supriyanti

2023

TELKOMNIKA

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The ideology of the Solo Batik pattern has not been conveyed to the public. In addition, a lot of people are unaware that batik contains particular patterns that are also used for particular activities. This study uses a convolutional neural network model to categorize 9 different Solo Batik patterns according to their use of elaborate geometric shapes, complicated symbols, patterns, dots, and natural designs. With 1 to 4 hidden layers, we aim to select the number of hidden layers that yields the highest accuracy. A 100×100 pixel image is used as the input. The feature extraction process then makes use of 3×3 feature maps from three convolution layers. The dropout regularization is then added, with settings ranging from 0.1 to 0.9. The Adam algorithm is also used in this model to perform optimization. The 3-layered convolutional neural networks (CNN) with a dropout value of 0.2, run in 20 epochs, produced accuracy results of 97.77%, which was the highest. Additionally, it can be inferred that applying a certain number of hidden layers and adding right dropout regularization values has an impact on raising the accuracy score.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm

Anggoro,

Marzuki,

Supriyanti

2023

TELKOMNIKA

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“…The prototype of HDCDSS is helpful for the diagnosis of patients' heart disease status depending on their current state/condition. Nikhil Gawande et al [3] proposed a heart disease classification system while making the use of CNN. The proposed system used 1D Convolution Neural Network to give Electrocardiography (ECG) classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification Comparison of Different Boosting Algorithms to Predict and Classify Conditions of Heart Disease

Mane,

Belgaonkar,

Dabhade

et al. 2023

Data Science and Intelligent Computing Techniques

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In the modern era, heart diseases account for the majority of the casualties. Timely and more efficient identification of every particular heart disease proves to be a vital factor in the healthcare sector. This paper presents a system which can predict and classify conditions of heart disease using several ML techniques called boosting algorithms. Cleveland Dataset is used to train this model. This particular dataset consists of 14 variables measured on 1025 individuals who have different health conditions and some having heart disease. The models used include boosting algorithms such as AdaBoost, XGboost, Gradient Boosting, CatBoost and LightGBM. We have compared all these boosting algorithms and calculated their respective accuracy and confusion matrices.

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“…They utilized complex QRS wave, T wave and P wave for assessing the sickness. The suggested to use CNN for recognizing distinctive heart disorders [1].…”

Section: ░ 2 Literature Reviewmentioning

confidence: 99%

Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine

Awati¹,

Patil²,

Kumbhar³

2021

IJEER

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Healthcare and disease detection in early stage is important in every human being. Proper and optimum detection of disease with smart controller is done using Particle swarm optimization (PSO) and Support Vector Machine (SVM). The research includes the Fuzzy Proportional Integral and Derivative (Fuzzy PID) controller was used with support vector machine to classify the heart disease. Particle Swarm Optimization is designed to remove the noise introduced in Electrocardiogram signal. Fuzzy PID controller was implemented for disease detection and prediction. Fuzzy PID controller provides most accurate and stable results.

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Heart diseases classification using convolutional neural network

Cited by 26 publications

References 8 publications

Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm

Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm

Classification Comparison of Different Boosting Algorithms to Predict and Classify Conditions of Heart Disease

Smart Heart Disease Detection using Particle Swarm Optimization and Support Vector Machine

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