Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique

Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithm...
Ausführliche Beschreibung

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Autor*in:	Bokhare, Anuja [verfasserIn] Bhagat, Aarti Bhalodia, Raj

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Artificial neural network (ANN) SMOTE functions Heart failure Perceptron and multi-layer perceptron MLP-SMOTE

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 4(2023), 2 vom: 25. Jan.
Übergeordnetes Werk:	volume:4 ; year:2023 ; number:2 ; day:25 ; month:01

Links:	Volltext

DOI / URN:	10.1007/s42979-022-01596-x

Katalog-ID:	SPR049646508

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520			\|a Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks.
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allfields	10.1007/s42979-022-01596-x doi (DE-627)SPR049646508 (SPR)s42979-022-01596-x-e DE-627 ger DE-627 rakwb eng Bokhare, Anuja verfasserin (orcid)0000-0001-9427-478X aut Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. Artificial neural network (ANN) (dpeaa)DE-He213 SMOTE functions (dpeaa)DE-He213 Heart failure (dpeaa)DE-He213 Perceptron and multi-layer perceptron (dpeaa)DE-He213 MLP-SMOTE (dpeaa)DE-He213 Bhagat, Aarti aut Bhalodia, Raj aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 2 vom: 25. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:2 day:25 month:01 https://dx.doi.org/10.1007/s42979-022-01596-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 2 25 01
spelling	10.1007/s42979-022-01596-x doi (DE-627)SPR049646508 (SPR)s42979-022-01596-x-e DE-627 ger DE-627 rakwb eng Bokhare, Anuja verfasserin (orcid)0000-0001-9427-478X aut Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. Artificial neural network (ANN) (dpeaa)DE-He213 SMOTE functions (dpeaa)DE-He213 Heart failure (dpeaa)DE-He213 Perceptron and multi-layer perceptron (dpeaa)DE-He213 MLP-SMOTE (dpeaa)DE-He213 Bhagat, Aarti aut Bhalodia, Raj aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 2 vom: 25. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:2 day:25 month:01 https://dx.doi.org/10.1007/s42979-022-01596-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 2 25 01
allfields_unstemmed	10.1007/s42979-022-01596-x doi (DE-627)SPR049646508 (SPR)s42979-022-01596-x-e DE-627 ger DE-627 rakwb eng Bokhare, Anuja verfasserin (orcid)0000-0001-9427-478X aut Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. Artificial neural network (ANN) (dpeaa)DE-He213 SMOTE functions (dpeaa)DE-He213 Heart failure (dpeaa)DE-He213 Perceptron and multi-layer perceptron (dpeaa)DE-He213 MLP-SMOTE (dpeaa)DE-He213 Bhagat, Aarti aut Bhalodia, Raj aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 2 vom: 25. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:2 day:25 month:01 https://dx.doi.org/10.1007/s42979-022-01596-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 2 25 01
allfieldsGer	10.1007/s42979-022-01596-x doi (DE-627)SPR049646508 (SPR)s42979-022-01596-x-e DE-627 ger DE-627 rakwb eng Bokhare, Anuja verfasserin (orcid)0000-0001-9427-478X aut Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. Artificial neural network (ANN) (dpeaa)DE-He213 SMOTE functions (dpeaa)DE-He213 Heart failure (dpeaa)DE-He213 Perceptron and multi-layer perceptron (dpeaa)DE-He213 MLP-SMOTE (dpeaa)DE-He213 Bhagat, Aarti aut Bhalodia, Raj aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 2 vom: 25. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:2 day:25 month:01 https://dx.doi.org/10.1007/s42979-022-01596-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 2 25 01
allfieldsSound	10.1007/s42979-022-01596-x doi (DE-627)SPR049646508 (SPR)s42979-022-01596-x-e DE-627 ger DE-627 rakwb eng Bokhare, Anuja verfasserin (orcid)0000-0001-9427-478X aut Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. Artificial neural network (ANN) (dpeaa)DE-He213 SMOTE functions (dpeaa)DE-He213 Heart failure (dpeaa)DE-He213 Perceptron and multi-layer perceptron (dpeaa)DE-He213 MLP-SMOTE (dpeaa)DE-He213 Bhagat, Aarti aut Bhalodia, Raj aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 2 vom: 25. Jan. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:2 day:25 month:01 https://dx.doi.org/10.1007/s42979-022-01596-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 2 25 01
language	English
source	Enthalten in SN Computer Science 4(2023), 2 vom: 25. Jan. volume:4 year:2023 number:2 day:25 month:01
sourceStr	Enthalten in SN Computer Science 4(2023), 2 vom: 25. Jan. volume:4 year:2023 number:2 day:25 month:01
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial neural network (ANN) SMOTE functions Heart failure Perceptron and multi-layer perceptron MLP-SMOTE
isfreeaccess_bool	false
container_title	SN Computer Science
authorswithroles_txt_mv	Bokhare, Anuja @@aut@@ Bhagat, Aarti @@aut@@ Bhalodia, Raj @@aut@@
publishDateDaySort_date	2023-01-25T00:00:00Z
hierarchy_top_id	1668832976
id	SPR049646508
language_de	englisch
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. 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author	Bokhare, Anuja
spellingShingle	Bokhare, Anuja misc Artificial neural network (ANN) misc SMOTE functions misc Heart failure misc Perceptron and multi-layer perceptron misc MLP-SMOTE Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique
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topic_title	Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique Artificial neural network (ANN) (dpeaa)DE-He213 SMOTE functions (dpeaa)DE-He213 Heart failure (dpeaa)DE-He213 Perceptron and multi-layer perceptron (dpeaa)DE-He213 MLP-SMOTE (dpeaa)DE-He213
topic	misc Artificial neural network (ANN) misc SMOTE functions misc Heart failure misc Perceptron and multi-layer perceptron misc MLP-SMOTE
topic_unstemmed	misc Artificial neural network (ANN) misc SMOTE functions misc Heart failure misc Perceptron and multi-layer perceptron misc MLP-SMOTE
topic_browse	misc Artificial neural network (ANN) misc SMOTE functions misc Heart failure misc Perceptron and multi-layer perceptron misc MLP-SMOTE
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title	Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique
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title_full	Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique
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title_sort	multi-layer perceptron for heart failure detection using smote technique
title_auth	Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique
abstract	Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Artificial neural networks (ANN) are now widely recognized as a powerful tool for many decision modeling problems. Many methods in this area, like multi-layer perceptron, backpropagation, feed-forward neural network, and many more, are used widely to solve many complex issues. ANN algorithms are also used to solve medical diagnosis to recognizing the disease, check MRI images to provide the results, diagnose heart disease, cancer, etc. Heart failure is considered one of the riskiest human diseases worldwide. In this paper, the MLP-SMOTE model is proposed to predict the failure of the heart using TensorFlow. The experimental result demonstrates that the system predicts heart illness with about 91.55% accuracy using neural networks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Multi-layer Perceptron for Heart Failure Detection Using SMOTE Technique
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