Explainable data mining model for hyperinsulinemia diagnostics

ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-secti...
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Autor*in:	Nevena Rankovic [verfasserIn] Dragica Rankovic [verfasserIn] Mirjana Ivanovic [verfasserIn] Igor Lukic [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	PCA FPCA K-Means SHAP Hyperinsulinemia

Übergeordnetes Werk:	In: Connection Science - Taylor & Francis Group, 2022, 36(2024), 1
Übergeordnetes Werk:	volume:36 ; year:2024 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/09540091.2024.2325496

Katalog-ID:	DOAJ095574999

Internformat


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allfields	10.1080/09540091.2024.2325496 doi (DE-627)DOAJ095574999 (DE-599)DOAJ3a5ea59d57bd4a61a3b36818c96ead8a DE-627 ger DE-627 rakwb eng QA75.5-76.95 Nevena Rankovic verfasserin aut Explainable data mining model for hyperinsulinemia diagnostics 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99. PCA FPCA K-Means SHAP Hyperinsulinemia Electronic computers. Computer science Dragica Rankovic verfasserin aut Mirjana Ivanovic verfasserin aut Igor Lukic verfasserin aut In Connection Science Taylor & Francis Group, 2022 36(2024), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:36 year:2024 number:1 https://doi.org/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/article/3a5ea59d57bd4a61a3b36818c96ead8a kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_100 GBV_ILN_101 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 36 2024 1
spelling	10.1080/09540091.2024.2325496 doi (DE-627)DOAJ095574999 (DE-599)DOAJ3a5ea59d57bd4a61a3b36818c96ead8a DE-627 ger DE-627 rakwb eng QA75.5-76.95 Nevena Rankovic verfasserin aut Explainable data mining model for hyperinsulinemia diagnostics 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99. PCA FPCA K-Means SHAP Hyperinsulinemia Electronic computers. Computer science Dragica Rankovic verfasserin aut Mirjana Ivanovic verfasserin aut Igor Lukic verfasserin aut In Connection Science Taylor & Francis Group, 2022 36(2024), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:36 year:2024 number:1 https://doi.org/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/article/3a5ea59d57bd4a61a3b36818c96ead8a kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_100 GBV_ILN_101 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 36 2024 1
allfields_unstemmed	10.1080/09540091.2024.2325496 doi (DE-627)DOAJ095574999 (DE-599)DOAJ3a5ea59d57bd4a61a3b36818c96ead8a DE-627 ger DE-627 rakwb eng QA75.5-76.95 Nevena Rankovic verfasserin aut Explainable data mining model for hyperinsulinemia diagnostics 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99. PCA FPCA K-Means SHAP Hyperinsulinemia Electronic computers. Computer science Dragica Rankovic verfasserin aut Mirjana Ivanovic verfasserin aut Igor Lukic verfasserin aut In Connection Science Taylor & Francis Group, 2022 36(2024), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:36 year:2024 number:1 https://doi.org/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/article/3a5ea59d57bd4a61a3b36818c96ead8a kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_100 GBV_ILN_101 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 36 2024 1
allfieldsGer	10.1080/09540091.2024.2325496 doi (DE-627)DOAJ095574999 (DE-599)DOAJ3a5ea59d57bd4a61a3b36818c96ead8a DE-627 ger DE-627 rakwb eng QA75.5-76.95 Nevena Rankovic verfasserin aut Explainable data mining model for hyperinsulinemia diagnostics 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99. PCA FPCA K-Means SHAP Hyperinsulinemia Electronic computers. Computer science Dragica Rankovic verfasserin aut Mirjana Ivanovic verfasserin aut Igor Lukic verfasserin aut In Connection Science Taylor & Francis Group, 2022 36(2024), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:36 year:2024 number:1 https://doi.org/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/article/3a5ea59d57bd4a61a3b36818c96ead8a kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_100 GBV_ILN_101 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 36 2024 1
allfieldsSound	10.1080/09540091.2024.2325496 doi (DE-627)DOAJ095574999 (DE-599)DOAJ3a5ea59d57bd4a61a3b36818c96ead8a DE-627 ger DE-627 rakwb eng QA75.5-76.95 Nevena Rankovic verfasserin aut Explainable data mining model for hyperinsulinemia diagnostics 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99. PCA FPCA K-Means SHAP Hyperinsulinemia Electronic computers. Computer science Dragica Rankovic verfasserin aut Mirjana Ivanovic verfasserin aut Igor Lukic verfasserin aut In Connection Science Taylor & Francis Group, 2022 36(2024), 1 (DE-627)306713276 (DE-600)1501040-5 13600494 nnns volume:36 year:2024 number:1 https://doi.org/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/article/3a5ea59d57bd4a61a3b36818c96ead8a kostenfrei https://www.tandfonline.com/doi/10.1080/09540091.2024.2325496 kostenfrei https://doaj.org/toc/0954-0091 Journal toc kostenfrei https://doaj.org/toc/1360-0494 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_100 GBV_ILN_101 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 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_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 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 36 2024 1
language	English
source	In Connection Science 36(2024), 1 volume:36 year:2024 number:1
sourceStr	In Connection Science 36(2024), 1 volume:36 year:2024 number:1
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	PCA FPCA K-Means SHAP Hyperinsulinemia Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Connection Science
authorswithroles_txt_mv	Nevena Rankovic @@aut@@ Dragica Rankovic @@aut@@ Mirjana Ivanovic @@aut@@ Igor Lukic @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	306713276
id	DOAJ095574999
language_de	englisch
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callnumber-first	Q - Science
author	Nevena Rankovic
spellingShingle	Nevena Rankovic misc QA75.5-76.95 misc PCA misc FPCA misc K-Means misc SHAP misc Hyperinsulinemia misc Electronic computers. Computer science Explainable data mining model for hyperinsulinemia diagnostics
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topic_title	QA75.5-76.95 Explainable data mining model for hyperinsulinemia diagnostics PCA FPCA K-Means SHAP Hyperinsulinemia
topic	misc QA75.5-76.95 misc PCA misc FPCA misc K-Means misc SHAP misc Hyperinsulinemia misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc PCA misc FPCA misc K-Means misc SHAP misc Hyperinsulinemia misc Electronic computers. Computer science
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title	Explainable data mining model for hyperinsulinemia diagnostics
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title_full	Explainable data mining model for hyperinsulinemia diagnostics
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author_browse	Nevena Rankovic Dragica Rankovic Mirjana Ivanovic Igor Lukic
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title_sort	explainable data mining model for hyperinsulinemia diagnostics
callnumber	QA75.5-76.95
title_auth	Explainable data mining model for hyperinsulinemia diagnostics
abstract	ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99.
abstractGer	ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99.
abstract_unstemmed	ABSTRACTIn our research, we present a data mining model for the early diagnosis of hyperinsulinemia, potentially reducing the risk of diabetes, heart disease, and other chronic conditions. The dataset, gathered from 2019 to 2022 by Serbia's Healthcare Center through an observational cross-sectional study, includes 1008 adolescents. Medical datasets are often highly imbalanced and may contain irrelevant features that hinder predictive performance. To address these challenges in the medical data analysis, we propose a model employing Functional Principal Component Analysis (FPCA), which also accounts for outliers that could otherwise lead to the inclusion of irrelevant features. Unlike standard Principal Component Analysis (PCA), which is sensitive to the initial positions of cluster centers influencing the final outcome, our model integrates FPCA with K-Means clustering to improve the preprocessing stage. Additionally, we have incorporated the post-hoc explanatory method SHAP (SHapley Additive exPlanations) alongside algorithms such as Random Forest, XGBoost, and LightGBM to provide deeper insights into our model, identifying the most contributory features for the development of hyperinsulinemia. Experimental results showed that combining FPCA with K-Means clustering enhances the accuracy of the XGBoost classifier, with this model achieving an accuracy score of 0.99.
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title_short	Explainable data mining model for hyperinsulinemia diagnostics
url	https://doi.org/10.1080/09540091.2024.2325496 https://doaj.org/article/3a5ea59d57bd4a61a3b36818c96ead8a https://www.tandfonline.com/doi/10.1080/09540091.2024.2325496 https://doaj.org/toc/0954-0091 https://doaj.org/toc/1360-0494
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up_date	2024-07-03T15:22:07.459Z
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code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4246</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">36</subfield><subfield code="j">2024</subfield><subfield code="e">1</subfield></datafield></record></collection>
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