PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound

Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Vankara, Jayavani [verfasserIn] Lavanya Devi, G. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	CVD DICE similarity coefficient Differential evolution approach GA

Übergeordnetes Werk:	Enthalten in: International journal of speech technology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 23(2020), 1 vom: 13. Jan., Seite 31-43
Übergeordnetes Werk:	volume:23 ; year:2020 ; number:1 ; day:13 ; month:01 ; pages:31-43

Links:	Volltext

DOI / URN:	10.1007/s10772-020-09670-6

Katalog-ID:	SPR013136747

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520			\|a Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods.
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allfields	10.1007/s10772-020-09670-6 doi (DE-627)SPR013136747 (SPR)s10772-020-09670-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Vankara, Jayavani verfasserin aut PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods. CVD (dpeaa)DE-He213 DICE similarity coefficient (dpeaa)DE-He213 Differential evolution approach (dpeaa)DE-He213 GA (dpeaa)DE-He213 Lavanya Devi, G. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 23(2020), 1 vom: 13. Jan., Seite 31-43 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:23 year:2020 number:1 day:13 month:01 pages:31-43 https://dx.doi.org/10.1007/s10772-020-09670-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-ANG SSG-OPC-ASE 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_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_120 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_2188 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_2548 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 17.00 ASE AR 23 2020 1 13 01 31-43
spelling	10.1007/s10772-020-09670-6 doi (DE-627)SPR013136747 (SPR)s10772-020-09670-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Vankara, Jayavani verfasserin aut PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods. CVD (dpeaa)DE-He213 DICE similarity coefficient (dpeaa)DE-He213 Differential evolution approach (dpeaa)DE-He213 GA (dpeaa)DE-He213 Lavanya Devi, G. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 23(2020), 1 vom: 13. Jan., Seite 31-43 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:23 year:2020 number:1 day:13 month:01 pages:31-43 https://dx.doi.org/10.1007/s10772-020-09670-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-ANG SSG-OPC-ASE 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_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_120 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_2188 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_2548 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 17.00 ASE AR 23 2020 1 13 01 31-43
allfields_unstemmed	10.1007/s10772-020-09670-6 doi (DE-627)SPR013136747 (SPR)s10772-020-09670-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Vankara, Jayavani verfasserin aut PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods. CVD (dpeaa)DE-He213 DICE similarity coefficient (dpeaa)DE-He213 Differential evolution approach (dpeaa)DE-He213 GA (dpeaa)DE-He213 Lavanya Devi, G. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 23(2020), 1 vom: 13. Jan., Seite 31-43 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:23 year:2020 number:1 day:13 month:01 pages:31-43 https://dx.doi.org/10.1007/s10772-020-09670-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-ANG SSG-OPC-ASE 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_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_120 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_2188 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_2548 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 17.00 ASE AR 23 2020 1 13 01 31-43
allfieldsGer	10.1007/s10772-020-09670-6 doi (DE-627)SPR013136747 (SPR)s10772-020-09670-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Vankara, Jayavani verfasserin aut PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods. CVD (dpeaa)DE-He213 DICE similarity coefficient (dpeaa)DE-He213 Differential evolution approach (dpeaa)DE-He213 GA (dpeaa)DE-He213 Lavanya Devi, G. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 23(2020), 1 vom: 13. Jan., Seite 31-43 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:23 year:2020 number:1 day:13 month:01 pages:31-43 https://dx.doi.org/10.1007/s10772-020-09670-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-ANG SSG-OPC-ASE 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_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_120 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_2188 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_2548 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 17.00 ASE AR 23 2020 1 13 01 31-43
allfieldsSound	10.1007/s10772-020-09670-6 doi (DE-627)SPR013136747 (SPR)s10772-020-09670-6-e DE-627 ger DE-627 rakwb eng 400 ASE 17.00 bkl Vankara, Jayavani verfasserin aut PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods. CVD (dpeaa)DE-He213 DICE similarity coefficient (dpeaa)DE-He213 Differential evolution approach (dpeaa)DE-He213 GA (dpeaa)DE-He213 Lavanya Devi, G. verfasserin aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 23(2020), 1 vom: 13. Jan., Seite 31-43 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:23 year:2020 number:1 day:13 month:01 pages:31-43 https://dx.doi.org/10.1007/s10772-020-09670-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA SSG-OPC-ANG SSG-OPC-ASE 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_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_120 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_2188 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_2548 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 17.00 ASE AR 23 2020 1 13 01 31-43
language	English
source	Enthalten in International journal of speech technology 23(2020), 1 vom: 13. Jan., Seite 31-43 volume:23 year:2020 number:1 day:13 month:01 pages:31-43
sourceStr	Enthalten in International journal of speech technology 23(2020), 1 vom: 13. Jan., Seite 31-43 volume:23 year:2020 number:1 day:13 month:01 pages:31-43
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	CVD DICE similarity coefficient Differential evolution approach GA
dewey-raw	400
isfreeaccess_bool	false
container_title	International journal of speech technology
authorswithroles_txt_mv	Vankara, Jayavani @@aut@@ Lavanya Devi, G. @@aut@@
publishDateDaySort_date	2020-01-13T00:00:00Z
hierarchy_top_id	27134895X
dewey-sort	3400
id	SPR013136747
language_de	englisch
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author	Vankara, Jayavani
spellingShingle	Vankara, Jayavani ddc 400 bkl 17.00 misc CVD misc DICE similarity coefficient misc Differential evolution approach misc GA PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound
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topic_title	400 ASE 17.00 bkl PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound CVD (dpeaa)DE-He213 DICE similarity coefficient (dpeaa)DE-He213 Differential evolution approach (dpeaa)DE-He213 GA (dpeaa)DE-He213
topic	ddc 400 bkl 17.00 misc CVD misc DICE similarity coefficient misc Differential evolution approach misc GA
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title	PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound
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title_full	PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound
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title_sort	paelc: predictive analysis by ensemble learning and classification heart disease detection using beat sound
title_auth	PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound
abstract	Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods.
abstractGer	Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods.
abstract_unstemmed	Abstract The computer-aided methods are certainly essential to perform clinical practices. The predictive analysis of disease scope from inputs recommended by the experts is one crucial dimension of the computer-aided clinical practices. The false alarming or delay in the detection of heart diseases is intolerable, which often experienced due to the lack of experience of the medical practitioners or pathologists. In this regard, considerable research is experiencing in the recent past to develop robust computer-aided predictive analysis methods for heart disease detection. In this regard, machine learning is playing a significant role. However, the contemporary methods built on machine learning strategies often landed with false alarming, which is due to the high dimensionality of the data projection is a given training corpus. With this argument, this manuscript endeavored to portray a novel ensemble learning strategy that enables high precision in heart disease prediction accuracy with minimal false alarming. The experimental study denotes the significance of the proposed model “Predictive Analysis by Ensemble Learning and Classification for Heart Disease Detection (PAELC)” compared to the other contemporary methods.
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title_short	PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound
url	https://dx.doi.org/10.1007/s10772-020-09670-6
remote_bool	true
author2	Lavanya Devi, G.
author2Str	Lavanya Devi, G.
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doi_str	10.1007/s10772-020-09670-6
up_date	2024-07-03T17:42:34.338Z
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PAELC: Predictive Analysis by Ensemble Learning and Classification heart disease detection using beat sound

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