A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments

Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Seba, P. Antony [verfasserIn] Benifa, J. V. Bibal

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Random forest (RF) AdaBoost (AB) Voting classifier CKD stage Predictive analytics

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Wireless personal communications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994, 126(2022), 1 vom: 12. Mai, Seite 581-604
Übergeordnetes Werk:	volume:126 ; year:2022 ; number:1 ; day:12 ; month:05 ; pages:581-604

Links:	Volltext

DOI / URN:	10.1007/s11277-022-09759-y

Katalog-ID:	SPR04807831X

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allfields	10.1007/s11277-022-09759-y doi (DE-627)SPR04807831X (SPR)s11277-022-09759-y-e DE-627 ger DE-627 rakwb eng Seba, P. Antony verfasserin aut A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. Random forest (RF) (dpeaa)DE-He213 AdaBoost (AB) (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 CKD stage (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Benifa, J. V. Bibal aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 1 vom: 12. Mai, Seite 581-604 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:1 day:12 month:05 pages:581-604 https://dx.doi.org/10.1007/s11277-022-09759-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_2119 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 AR 126 2022 1 12 05 581-604
spelling	10.1007/s11277-022-09759-y doi (DE-627)SPR04807831X (SPR)s11277-022-09759-y-e DE-627 ger DE-627 rakwb eng Seba, P. Antony verfasserin aut A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. Random forest (RF) (dpeaa)DE-He213 AdaBoost (AB) (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 CKD stage (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Benifa, J. V. Bibal aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 1 vom: 12. Mai, Seite 581-604 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:1 day:12 month:05 pages:581-604 https://dx.doi.org/10.1007/s11277-022-09759-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_2119 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 AR 126 2022 1 12 05 581-604
allfields_unstemmed	10.1007/s11277-022-09759-y doi (DE-627)SPR04807831X (SPR)s11277-022-09759-y-e DE-627 ger DE-627 rakwb eng Seba, P. Antony verfasserin aut A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. Random forest (RF) (dpeaa)DE-He213 AdaBoost (AB) (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 CKD stage (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Benifa, J. V. Bibal aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 1 vom: 12. Mai, Seite 581-604 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:1 day:12 month:05 pages:581-604 https://dx.doi.org/10.1007/s11277-022-09759-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_2119 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 AR 126 2022 1 12 05 581-604
allfieldsGer	10.1007/s11277-022-09759-y doi (DE-627)SPR04807831X (SPR)s11277-022-09759-y-e DE-627 ger DE-627 rakwb eng Seba, P. Antony verfasserin aut A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. Random forest (RF) (dpeaa)DE-He213 AdaBoost (AB) (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 CKD stage (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Benifa, J. V. Bibal aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 1 vom: 12. Mai, Seite 581-604 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:1 day:12 month:05 pages:581-604 https://dx.doi.org/10.1007/s11277-022-09759-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_2119 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 AR 126 2022 1 12 05 581-604
allfieldsSound	10.1007/s11277-022-09759-y doi (DE-627)SPR04807831X (SPR)s11277-022-09759-y-e DE-627 ger DE-627 rakwb eng Seba, P. Antony verfasserin aut A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. Random forest (RF) (dpeaa)DE-He213 AdaBoost (AB) (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 CKD stage (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213 Benifa, J. V. Bibal aut Enthalten in Wireless personal communications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 126(2022), 1 vom: 12. Mai, Seite 581-604 (DE-627)271179120 (DE-600)1479327-1 1572-834X nnns volume:126 year:2022 number:1 day:12 month:05 pages:581-604 https://dx.doi.org/10.1007/s11277-022-09759-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_2119 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 AR 126 2022 1 12 05 581-604
language	English
source	Enthalten in Wireless personal communications 126(2022), 1 vom: 12. Mai, Seite 581-604 volume:126 year:2022 number:1 day:12 month:05 pages:581-604
sourceStr	Enthalten in Wireless personal communications 126(2022), 1 vom: 12. Mai, Seite 581-604 volume:126 year:2022 number:1 day:12 month:05 pages:581-604
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Random forest (RF) AdaBoost (AB) Voting classifier CKD stage Predictive analytics
isfreeaccess_bool	false
container_title	Wireless personal communications
authorswithroles_txt_mv	Seba, P. Antony @@aut@@ Benifa, J. V. Bibal @@aut@@
publishDateDaySort_date	2022-05-12T00:00:00Z
hierarchy_top_id	271179120
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author	Seba, P. Antony
spellingShingle	Seba, P. Antony misc Random forest (RF) misc AdaBoost (AB) misc Voting classifier misc CKD stage misc Predictive analytics A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments
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topic_title	A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments Random forest (RF) (dpeaa)DE-He213 AdaBoost (AB) (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 CKD stage (dpeaa)DE-He213 Predictive analytics (dpeaa)DE-He213
topic	misc Random forest (RF) misc AdaBoost (AB) misc Voting classifier misc CKD stage misc Predictive analytics
topic_unstemmed	misc Random forest (RF) misc AdaBoost (AB) misc Voting classifier misc CKD stage misc Predictive analytics
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title	A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments
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title_full	A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments
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title_sort	hybrid analytic model for the effective prediction of different stages in chronic kidney ailments
title_auth	A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments
abstract	Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract Chronic kidney disease (CKD) is a gradual loss of kidney function over the period of time and it is irrevocable once functionality reaches the critical state. Detecting the various stages of CKD helps to reduce the progression of the disease. Accurate prediction of CKD stages is one of the urgent needs in the medical industry and it can be effectively done by adopting machine learning (ML) techniques. The primary objective of the present research is to develop an effective classification model for the accurate prediction of CKD stages based on the patient’s health profile as well as the clinical test reports. Here, a hybrid ML strategy is employed that integrates random forest (RF) and AdaBoost (AB) techniques through a voting classifier (VC). The standard CKD dataset with 400 tuples and 25 parameters is used for the proposed investigation. The modification of diet in renal disease (MDRD) equation is used to extract an additional feature known as “estimated Glomerular Filtration Rate (eGFR)” for the prediction of the CKD stage. Pre-processing is carried out on the CKD dataset to fill the missing values by considering the skewness of the parameters and the issue of data leakage is also well addressed. Medically important features are considered and Correlation analysis is carried out to select the appropriate features for the model building process. The proposed Hybrid Ensemble Model (HEM) aids in lowering the bias and variance. HEM model efficiency is assessed using the performance metrics such as cross validation score (CVS), accuracy, precision, recall, F1 measure, Mean Squared Error (MSE), bias and variance and it is compared with the state-of-the-art classification schemes. The outcomes of the analysis reveal that the proposed HEM ensures that the CKD stage prediction is more accurate with 99.16%, 100%, 100% in reduced feature set I, set II, set III and with cross validation score of 97.85%, 99.28%, and 99.64% with reduced features set I, set II and set III respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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title_short	A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments
url	https://dx.doi.org/10.1007/s11277-022-09759-y
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author2	Benifa, J. V. Bibal
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doi_str	10.1007/s11277-022-09759-y
up_date	2024-07-03T16:51:29.023Z
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