A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare

Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kosolwattana, Tanapol [verfasserIn] Liu, Chenang Hu, Renjie Han, Shizhong Chen, Hua Lin, Ying

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Imbalanced data classification in healthcare SMOTE-based resampling Adaptive nearest neighborhood selection Self-inspection

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: BioData Mining - London : BioMed Central, 2008, 16(2023), 1 vom: 25. Apr.
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:1 ; day:25 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s13040-023-00330-4

Katalog-ID:	SPR05018525X

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520			\|a Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data.
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allfields	10.1186/s13040-023-00330-4 doi (DE-627)SPR05018525X (SPR)s13040-023-00330-4-e DE-627 ger DE-627 rakwb eng Kosolwattana, Tanapol verfasserin aut A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. Imbalanced data classification in healthcare (dpeaa)DE-He213 SMOTE-based resampling (dpeaa)DE-He213 Adaptive nearest neighborhood selection (dpeaa)DE-He213 Self-inspection (dpeaa)DE-He213 Liu, Chenang aut Hu, Renjie aut Han, Shizhong aut Chen, Hua aut Lin, Ying aut Enthalten in BioData Mining London : BioMed Central, 2008 16(2023), 1 vom: 25. Apr. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:16 year:2023 number:1 day:25 month:04 https://dx.doi.org/10.1186/s13040-023-00330-4 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 04
spelling	10.1186/s13040-023-00330-4 doi (DE-627)SPR05018525X (SPR)s13040-023-00330-4-e DE-627 ger DE-627 rakwb eng Kosolwattana, Tanapol verfasserin aut A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. Imbalanced data classification in healthcare (dpeaa)DE-He213 SMOTE-based resampling (dpeaa)DE-He213 Adaptive nearest neighborhood selection (dpeaa)DE-He213 Self-inspection (dpeaa)DE-He213 Liu, Chenang aut Hu, Renjie aut Han, Shizhong aut Chen, Hua aut Lin, Ying aut Enthalten in BioData Mining London : BioMed Central, 2008 16(2023), 1 vom: 25. Apr. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:16 year:2023 number:1 day:25 month:04 https://dx.doi.org/10.1186/s13040-023-00330-4 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 04
allfields_unstemmed	10.1186/s13040-023-00330-4 doi (DE-627)SPR05018525X (SPR)s13040-023-00330-4-e DE-627 ger DE-627 rakwb eng Kosolwattana, Tanapol verfasserin aut A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. Imbalanced data classification in healthcare (dpeaa)DE-He213 SMOTE-based resampling (dpeaa)DE-He213 Adaptive nearest neighborhood selection (dpeaa)DE-He213 Self-inspection (dpeaa)DE-He213 Liu, Chenang aut Hu, Renjie aut Han, Shizhong aut Chen, Hua aut Lin, Ying aut Enthalten in BioData Mining London : BioMed Central, 2008 16(2023), 1 vom: 25. Apr. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:16 year:2023 number:1 day:25 month:04 https://dx.doi.org/10.1186/s13040-023-00330-4 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 04
allfieldsGer	10.1186/s13040-023-00330-4 doi (DE-627)SPR05018525X (SPR)s13040-023-00330-4-e DE-627 ger DE-627 rakwb eng Kosolwattana, Tanapol verfasserin aut A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. Imbalanced data classification in healthcare (dpeaa)DE-He213 SMOTE-based resampling (dpeaa)DE-He213 Adaptive nearest neighborhood selection (dpeaa)DE-He213 Self-inspection (dpeaa)DE-He213 Liu, Chenang aut Hu, Renjie aut Han, Shizhong aut Chen, Hua aut Lin, Ying aut Enthalten in BioData Mining London : BioMed Central, 2008 16(2023), 1 vom: 25. Apr. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:16 year:2023 number:1 day:25 month:04 https://dx.doi.org/10.1186/s13040-023-00330-4 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 04
allfieldsSound	10.1186/s13040-023-00330-4 doi (DE-627)SPR05018525X (SPR)s13040-023-00330-4-e DE-627 ger DE-627 rakwb eng Kosolwattana, Tanapol verfasserin aut A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. Imbalanced data classification in healthcare (dpeaa)DE-He213 SMOTE-based resampling (dpeaa)DE-He213 Adaptive nearest neighborhood selection (dpeaa)DE-He213 Self-inspection (dpeaa)DE-He213 Liu, Chenang aut Hu, Renjie aut Han, Shizhong aut Chen, Hua aut Lin, Ying aut Enthalten in BioData Mining London : BioMed Central, 2008 16(2023), 1 vom: 25. Apr. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:16 year:2023 number:1 day:25 month:04 https://dx.doi.org/10.1186/s13040-023-00330-4 kostenfrei 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_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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2023 1 25 04
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source	Enthalten in BioData Mining 16(2023), 1 vom: 25. Apr. volume:16 year:2023 number:1 day:25 month:04
sourceStr	Enthalten in BioData Mining 16(2023), 1 vom: 25. Apr. volume:16 year:2023 number:1 day:25 month:04
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topic_facet	Imbalanced data classification in healthcare SMOTE-based resampling Adaptive nearest neighborhood selection Self-inspection
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authorswithroles_txt_mv	Kosolwattana, Tanapol @@aut@@ Liu, Chenang @@aut@@ Hu, Renjie @@aut@@ Han, Shizhong @@aut@@ Chen, Hua @@aut@@ Lin, Ying @@aut@@
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author	Kosolwattana, Tanapol
spellingShingle	Kosolwattana, Tanapol misc Imbalanced data classification in healthcare misc SMOTE-based resampling misc Adaptive nearest neighborhood selection misc Self-inspection A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
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topic_title	A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare Imbalanced data classification in healthcare (dpeaa)DE-He213 SMOTE-based resampling (dpeaa)DE-He213 Adaptive nearest neighborhood selection (dpeaa)DE-He213 Self-inspection (dpeaa)DE-He213
topic	misc Imbalanced data classification in healthcare misc SMOTE-based resampling misc Adaptive nearest neighborhood selection misc Self-inspection
topic_unstemmed	misc Imbalanced data classification in healthcare misc SMOTE-based resampling misc Adaptive nearest neighborhood selection misc Self-inspection
topic_browse	misc Imbalanced data classification in healthcare misc SMOTE-based resampling misc Adaptive nearest neighborhood selection misc Self-inspection
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title	A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
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title_full	A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
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author_browse	Kosolwattana, Tanapol Liu, Chenang Hu, Renjie Han, Shizhong Chen, Hua Lin, Ying
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title_sort	self-inspected adaptive smote algorithm (sasmote) for highly imbalanced data classification in healthcare
title_auth	A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
abstract	Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. © The Author(s) 2023
abstractGer	Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. © The Author(s) 2023
abstract_unstemmed	Abstract In many healthcare applications, datasets for classification may be highly imbalanced due to the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority Over-sampling Technique) algorithm has been developed as an effective resampling method for imbalanced data classification by oversampling samples from the minority class. However, samples generated by SMOTE may be ambiguous, low-quality and non-separable with the majority class. To enhance the quality of generated samples, we proposed a novel self-inspected adaptive SMOTE (SASMOTE) model that leverages an adaptive nearest neighborhood selection algorithm to identify the “visible” nearest neighbors, which are used to generate samples likely to fall into the minority class. To further enhance the quality of the generated samples, an uncertainty elimination via self-inspection approach is introduced in the proposed SASMOTE model. Its objective is to filter out the generated samples that are highly uncertain and inseparable with the majority class. The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. By generating the higher quality synthetic samples, the proposed algorithm is able to help achieve better prediction performance (in terms of F1 score) on average compared to the other methods, which is promising to enhance the usability of machine learning models on highly imbalanced healthcare data. © The Author(s) 2023
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title_short	A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
url	https://dx.doi.org/10.1186/s13040-023-00330-4
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author2	Liu, Chenang Hu, Renjie Han, Shizhong Chen, Hua Lin, Ying
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up_date	2024-07-03T13:55:14.493Z
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The effectiveness of the proposed algorithm is compared with existing SMOTE-based algorithms and demonstrated through two real-world case studies in healthcare, including risk gene discovery and fatal congenital heart disease prediction. 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A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare

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