Classification of heart disease based on PCG signal using CNN

Sugiyarto, Aditya Wisnugraha; Abadi, Agus Maman; Sumarna, Sumarna

doi:10.12928/telkomnika.v19i5.20486

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…The features selecting by sequential feature selection algorithm among the 13 heart disease dataset features in the heart disease dataset are the following: The better combination of features selected by the proposed approach are as: Best combination (highest accuracy achieved: 0.971): (0, 1,2,4,6,7,8,9,11,12).…”

Section: Resultsmentioning

confidence: 99%

An improved feature selection approach for chronic heart disease detection

Sushma

Assegie²,

Vinutha

et al. 2021

Bulletin EEI

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Irrelevant feature in heart disease dataset affects the performance of binary classification model. Consequently, eliminating irrelevant and redundant feature (s) from training set with feature selection algorithm significantly improves the performance of classification model on heart disease detection. Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal features. Furthermore, exhaustive and permutation based feature selection algorithm are implemented and compared with SFS algorithm. The implemented and existing feature selection algorithms are evaluated using real world Pima Indian heart disease dataset and result appears to prove that the SFS algorithm outperforms as compared to exhaustive and permutation based feature selection algorithm. Overall, the result looks promising and more effective heart disease detection model is developed with accuracy of 99.3%.

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Section: Resultsmentioning

confidence: 99%

An improved feature selection approach for chronic heart disease detection

Sushma

Assegie²,

Vinutha

et al. 2021

Bulletin EEI

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“…Image captioning aims to create descriptive sentences that complement the image [15]. Convolutional neural network (CNN) has been widely used in captioning work [16], [17] since their popularity in dealing with computer vision problems such as classification [18]- [25] and object detection [26]- [30]. The ability of the long short-term memory (LSTM) network to learn order dependencies in sequence prediction problems in data series [31]- [36] makes it widely used for captioning tasks in generating sentence predictions.…”

Section: Introductionmentioning

confidence: 99%

Image captioning to aid blind and visually impaired outdoor navigation

Faurina

Jelita

Vatresia

et al. 2023

IJ-AI

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Artificial intelligence technology has dramatically improved the quality of services for human needs, one of which is technology to improve the quality of services for the blind and visually impaired, particularly technology that can help them understand visual sights to facilitate navigation in their daily lives. This study developed an image captioning model to aid the blind and visually impaired in outdoor navigation. The image captioning model employs the encoder-decoder method, with the convolutional neural network (CNN) feature extraction and attention layer as encoders and the long short-term memory (LSTM) as decoders. ResNet101 and ResNet152 are used in the encoder to extract image features. The results of the extraction and caption are forwarded to the attention layer and the LSTM network. The attention layer uses the Bahdanau attention mechanism. The accuracy of the model is calculated using the bilingual evaluation understudy score (BLEU), metric for evaluation of translation with explicit ordering (METEOR) and recall-oriented understudy for gisting evaluation-longest common subsequence (ROUGE-L). ResNet101 performed the best on BLEU-4, scoring 91.811% and 94.0337% in the METEOR evaluation. The captioning results show that the model is quite successful in displaying a simple caption that is suitable for each image.

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“…Common symptoms include shortness of breath, swollen ankles, and fatigue, while signs such as high jugular venous pressure, pulmonary crackles, and peripheral edema may also be present, indicating structural and/or functional cardiac or non-cardiac abnormalities [2], [3]. In Indonesia, heart disease is the leading cause of death, and HF represents a significant portion of these cases [4]. Approximately 5% of the country's population is estimated to suffer from HF [5].…”

Section: Introductionmentioning

confidence: 99%

Integration of PSO-based advanced supervised learning techniques for classification data mining to predict heart failure

Mesran,

Kmurawak,

Windarto

2023

TELKOMNIKA

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Heart failure (HF) is a global health threat, requiring urgent research in its classification. This study proposes a novel approach for HF classification by integrating advanced supervised learning (ASL) and particle swarm optimization (PSO). ASL techniques like bagging and AdaBoost are employed within the PSO+ASL optimization model to enhance prediction accuracy. PSO optimizes model weights and bias, while ASL addresses overfitting or underfitting issues. Split validation and cross-validation (70:30, 80:20, 90:10 with k-fold=10) are used for further optimization. The testing phase involves 12 classifiers in five groups: decision tree models (DTM), support vector machines (SVM), Naïve Bayes classifiers models (NBCM), logistic regression models (LRM), and lazy model (LM). Evaluating the proposed approach with an HF patient dataset from https://www.kaggle.com, results are compared against the standard model, PSO optimization, and PSO+ASL. Experimental findings demonstrate the superiority of the proposed approach, achieving higher accuracy in HF prediction. The PSO+ASL optimization model with the k-nearest neighbor (k-NN) method exhibits the best classification performance. It consistently achieves the highest accuracy across all tests on dataset composition ratios, with 100% accuracy, f-measure, sensitivity, specificity values, and area under cover (AUC) of 1. The proposed approach serves as a reliable tool for early detection and prevention of HF.

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Classification of heart disease based on PCG signal using CNN

Cited by 7 publications

References 30 publications

An improved feature selection approach for chronic heart disease detection

An improved feature selection approach for chronic heart disease detection

Image captioning to aid blind and visually impaired outdoor navigation

Integration of PSO-based advanced supervised learning techniques for classification data mining to predict heart failure

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