Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S

Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Tzong-Hann Yang [verfasserIn] Yu-Fu Chen [verfasserIn] Yen-Fu Cheng [verfasserIn] Jue-Ni Huang [verfasserIn] Chuan-Song Wu [verfasserIn] Yuan-Chia Chu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement

Übergeordnetes Werk:	In: BioData Mining - BMC, 2010, 16(2023), 1, Seite 17
Übergeordnetes Werk:	volume:16 ; year:2023 ; number:1 ; pages:17

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s13040-023-00351-z

Katalog-ID:	DOAJ099152894

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520			\|a Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
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allfields	10.1186/s13040-023-00351-z doi (DE-627)DOAJ099152894 (DE-599)DOAJ6d4672723e664574a3b0234b6ad337e2 DE-627 ger DE-627 rakwb eng R858-859.7 QA299.6-433 Tzong-Hann Yang verfasserin aut Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement Computer applications to medicine. Medical informatics Analysis Yu-Fu Chen verfasserin aut Yen-Fu Cheng verfasserin aut Jue-Ni Huang verfasserin aut Chuan-Song Wu verfasserin aut Yuan-Chia Chu verfasserin aut In BioData Mining BMC, 2010 16(2023), 1, Seite 17 (DE-627)572421893 (DE-600)2438773-3 17560381 nnns volume:16 year:2023 number:1 pages:17 https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/article/6d4672723e664574a3b0234b6ad337e2 kostenfrei https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/toc/1756-0381 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_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 17
spelling	10.1186/s13040-023-00351-z doi (DE-627)DOAJ099152894 (DE-599)DOAJ6d4672723e664574a3b0234b6ad337e2 DE-627 ger DE-627 rakwb eng R858-859.7 QA299.6-433 Tzong-Hann Yang verfasserin aut Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement Computer applications to medicine. Medical informatics Analysis Yu-Fu Chen verfasserin aut Yen-Fu Cheng verfasserin aut Jue-Ni Huang verfasserin aut Chuan-Song Wu verfasserin aut Yuan-Chia Chu verfasserin aut In BioData Mining BMC, 2010 16(2023), 1, Seite 17 (DE-627)572421893 (DE-600)2438773-3 17560381 nnns volume:16 year:2023 number:1 pages:17 https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/article/6d4672723e664574a3b0234b6ad337e2 kostenfrei https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/toc/1756-0381 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_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 17
allfields_unstemmed	10.1186/s13040-023-00351-z doi (DE-627)DOAJ099152894 (DE-599)DOAJ6d4672723e664574a3b0234b6ad337e2 DE-627 ger DE-627 rakwb eng R858-859.7 QA299.6-433 Tzong-Hann Yang verfasserin aut Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement Computer applications to medicine. Medical informatics Analysis Yu-Fu Chen verfasserin aut Yen-Fu Cheng verfasserin aut Jue-Ni Huang verfasserin aut Chuan-Song Wu verfasserin aut Yuan-Chia Chu verfasserin aut In BioData Mining BMC, 2010 16(2023), 1, Seite 17 (DE-627)572421893 (DE-600)2438773-3 17560381 nnns volume:16 year:2023 number:1 pages:17 https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/article/6d4672723e664574a3b0234b6ad337e2 kostenfrei https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/toc/1756-0381 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_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 17
allfieldsGer	10.1186/s13040-023-00351-z doi (DE-627)DOAJ099152894 (DE-599)DOAJ6d4672723e664574a3b0234b6ad337e2 DE-627 ger DE-627 rakwb eng R858-859.7 QA299.6-433 Tzong-Hann Yang verfasserin aut Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement Computer applications to medicine. Medical informatics Analysis Yu-Fu Chen verfasserin aut Yen-Fu Cheng verfasserin aut Jue-Ni Huang verfasserin aut Chuan-Song Wu verfasserin aut Yuan-Chia Chu verfasserin aut In BioData Mining BMC, 2010 16(2023), 1, Seite 17 (DE-627)572421893 (DE-600)2438773-3 17560381 nnns volume:16 year:2023 number:1 pages:17 https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/article/6d4672723e664574a3b0234b6ad337e2 kostenfrei https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/toc/1756-0381 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_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 17
allfieldsSound	10.1186/s13040-023-00351-z doi (DE-627)DOAJ099152894 (DE-599)DOAJ6d4672723e664574a3b0234b6ad337e2 DE-627 ger DE-627 rakwb eng R858-859.7 QA299.6-433 Tzong-Hann Yang verfasserin aut Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely. Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement Computer applications to medicine. Medical informatics Analysis Yu-Fu Chen verfasserin aut Yen-Fu Cheng verfasserin aut Jue-Ni Huang verfasserin aut Chuan-Song Wu verfasserin aut Yuan-Chia Chu verfasserin aut In BioData Mining BMC, 2010 16(2023), 1, Seite 17 (DE-627)572421893 (DE-600)2438773-3 17560381 nnns volume:16 year:2023 number:1 pages:17 https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/article/6d4672723e664574a3b0234b6ad337e2 kostenfrei https://doi.org/10.1186/s13040-023-00351-z kostenfrei https://doaj.org/toc/1756-0381 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_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 17
language	English
source	In BioData Mining 16(2023), 1, Seite 17 volume:16 year:2023 number:1 pages:17
sourceStr	In BioData Mining 16(2023), 1, Seite 17 volume:16 year:2023 number:1 pages:17
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement Computer applications to medicine. Medical informatics Analysis
isfreeaccess_bool	true
container_title	BioData Mining
authorswithroles_txt_mv	Tzong-Hann Yang @@aut@@ Yu-Fu Chen @@aut@@ Yen-Fu Cheng @@aut@@ Jue-Ni Huang @@aut@@ Chuan-Song Wu @@aut@@ Yuan-Chia Chu @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	572421893
id	DOAJ099152894
language_de	englisch
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Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. 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callnumber-first	R - Medicine
author	Tzong-Hann Yang
spellingShingle	Tzong-Hann Yang misc R858-859.7 misc QA299.6-433 misc Age-related misc Hearing loss misc LGBM misc Machine learning misc HHIE-S misc Predictive enhancement misc Computer applications to medicine. Medical informatics misc Analysis Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S
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topic_title	R858-859.7 QA299.6-433 Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S Age-related Hearing loss LGBM Machine learning HHIE-S Predictive enhancement
topic	misc R858-859.7 misc QA299.6-433 misc Age-related misc Hearing loss misc LGBM misc Machine learning misc HHIE-S misc Predictive enhancement misc Computer applications to medicine. Medical informatics misc Analysis
topic_unstemmed	misc R858-859.7 misc QA299.6-433 misc Age-related misc Hearing loss misc LGBM misc Machine learning misc HHIE-S misc Predictive enhancement misc Computer applications to medicine. Medical informatics misc Analysis
topic_browse	misc R858-859.7 misc QA299.6-433 misc Age-related misc Hearing loss misc LGBM misc Machine learning misc HHIE-S misc Predictive enhancement misc Computer applications to medicine. Medical informatics misc Analysis
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title	Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S
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title_full	Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S
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author_browse	Tzong-Hann Yang Yu-Fu Chen Yen-Fu Cheng Jue-Ni Huang Chuan-Song Wu Yuan-Chia Chu
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title_sort	optimizing age-related hearing risk predictions: an advanced machine learning integration with hhie-s
callnumber	R858-859.7
title_auth	Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S
abstract	Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
abstractGer	Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
abstract_unstemmed	Abstract Objectives The elderly are disproportionately affected by age-related hearing loss (ARHL). Despite being a well-known tool for ARHL evaluation, the Hearing Handicap Inventory for the Elderly Screening version (HHIE-S) has only traditionally been used for direct screening using self-reported outcomes. This work uses a novel integration of machine learning approaches to improve the predicted accuracy of the HHIE-S tool for ARHL in older adults. Methods We employed a dataset that was gathered between 2016 and 2018 and included 1,526 senior citizens from several Taipei City Hospital branches. 80% of the data were used for training (n = 1220) and 20% were used for testing (n = 356). XGBoost, Gradient Boosting, and LightGBM were among the machine learning models that were only used and assessed on the training set. In order to prevent data leakage and overfitting, the Light Gradient Boosting Machine (LGBM) model—which had the greatest AUC of 0.83 (95% CI 0.81–0.85)—was then only used on the holdout testing data. Results On the testing set, the LGBM model showed a strong AUC of 0.82 (95% CI 0.79–0.86), far outperforming conventional techniques. Notably, several HHIE-S items and age were found to be significant characteristics. In contrast to traditional HHIE research, which concentrates on the psychological effects of hearing loss, this study combines cutting-edge machine learning techniques—specifically, the LGBM classifier—with the HHIE-S tool. The incorporation of SHAP values enhances the interpretability of the model's predictions and provides a more comprehensive comprehension of the significance of various aspects. Conclusions Our methodology highlights the great potential that arises from combining machine learning with validated hearing evaluation instruments such as the HHIE-S. Healthcare practitioners can anticipate ARHL more accurately thanks to this integration, which makes it easier to intervene quickly and precisely.
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title_short	Optimizing age-related hearing risk predictions: an advanced machine learning integration with HHIE-S
url	https://doi.org/10.1186/s13040-023-00351-z https://doaj.org/article/6d4672723e664574a3b0234b6ad337e2 https://doaj.org/toc/1756-0381
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