An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis
Diabetic ketoacidosis (DKA) is a serious complication that affects millions of individuals globally and presents significant health complications. Hyperchloremia, an electrolyte imbalance characterized by high levels of chloride in the blood, may result in gastrointestinal problems, kidney damage, a...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10384375/ |
| _version_ | 1797348737433993216 |
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| author | George Obaido Blessing Ogbuokiri Chidozie Williams Chukwu Fadekemi Janet Osaye Oluwaseun Francis Egbelowo Mark Izuchukwu Uzochukwu Ibomoiye Domor Mienye Kehinde Aruleba Mpho Primus Okechinyere Achilonu |
| author_facet | George Obaido Blessing Ogbuokiri Chidozie Williams Chukwu Fadekemi Janet Osaye Oluwaseun Francis Egbelowo Mark Izuchukwu Uzochukwu Ibomoiye Domor Mienye Kehinde Aruleba Mpho Primus Okechinyere Achilonu |
| author_sort | George Obaido |
| collection | DOAJ |
| description | Diabetic ketoacidosis (DKA) is a serious complication that affects millions of individuals globally and presents significant health complications. Hyperchloremia, an electrolyte imbalance characterized by high levels of chloride in the blood, may result in gastrointestinal problems, kidney damage, and even death, especially in DKA patients. Early detection and treatment of hyperchloremia are of utmost importance in the management of DKA. This study explores the potential of the bootstrap aggregating ensemble with random subspaces machine learning approach to predict the occurrence of hyperchloremia, providing a basis for early intervention and improved patient outcomes. We tested our approach with the retrospective MIMIC-III database containing 1177 DKA patients and compared it with previous studies with an area under the curve (AUC) of 100%. Our approach showed significant performance outperforming other methods. The combination of this approach may enhance the early detection and timely intervention of hyperchloremia cases, ultimately leading to improved patient outcomes and a more effective management of DKA-associated complications. Our work aims to contribute to the development of decision support tools for healthcare professionals, assisting them in making informed decisions for DKA patients, with a focus on preventing and managing hyperchloremia. |
| first_indexed | 2024-03-08T12:09:19Z |
| format | Article |
| id | doaj.art-0d2aacf790f8482b9ceee36e1c0ce9e5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T12:09:19Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0d2aacf790f8482b9ceee36e1c0ce9e52024-01-23T00:02:05ZengIEEEIEEE Access2169-35362024-01-01129536954910.1109/ACCESS.2024.335118810384375An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic KetoacidosisGeorge Obaido0Blessing Ogbuokiri1https://orcid.org/0000-0003-1606-0019Chidozie Williams Chukwu2Fadekemi Janet Osaye3https://orcid.org/0000-0002-1595-1947Oluwaseun Francis Egbelowo4https://orcid.org/0000-0002-5636-4642Mark Izuchukwu Uzochukwu5Ibomoiye Domor Mienye6Kehinde Aruleba7Mpho Primus8https://orcid.org/0000-0002-5270-3619Okechinyere Achilonu9Center for Human-Compatible Artificial Intelligence (CHAI), Berkeley Institute for Data Science (BIDS), University of California at Berkeley, Berkeley, CA, USADepartment of Mathematics and Statistics, York University, Toronto, CanadaDepartment of Mathematics, Wake Forest University, Winston-Salem, NC, USADepartment of Mathematics and Computer Science, Alabama State University, Montgomery, AL, USADepartment of Integrative Biology, The University of Texas at Austin, Austin, TX, USADepartment of Mathematics and Computer Science, Alabama State University, Montgomery, AL, USADepartment of Information Technology, Walter Sisulu University, Buffalo City Campus, East London, South AfricaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester, U.K.Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South AfricaSchool of Public Health, University of the Witwatersrand, Johannesburg, South AfricaDiabetic ketoacidosis (DKA) is a serious complication that affects millions of individuals globally and presents significant health complications. Hyperchloremia, an electrolyte imbalance characterized by high levels of chloride in the blood, may result in gastrointestinal problems, kidney damage, and even death, especially in DKA patients. Early detection and treatment of hyperchloremia are of utmost importance in the management of DKA. This study explores the potential of the bootstrap aggregating ensemble with random subspaces machine learning approach to predict the occurrence of hyperchloremia, providing a basis for early intervention and improved patient outcomes. We tested our approach with the retrospective MIMIC-III database containing 1177 DKA patients and compared it with previous studies with an area under the curve (AUC) of 100%. Our approach showed significant performance outperforming other methods. The combination of this approach may enhance the early detection and timely intervention of hyperchloremia cases, ultimately leading to improved patient outcomes and a more effective management of DKA-associated complications. Our work aims to contribute to the development of decision support tools for healthcare professionals, assisting them in making informed decisions for DKA patients, with a focus on preventing and managing hyperchloremia.https://ieeexplore.ieee.org/document/10384375/Boosting aggregating or bagging classifierdiabetic ketoacidosis (DKA)hyperchloremiamachine learningpredictive modeling |
| spellingShingle | George Obaido Blessing Ogbuokiri Chidozie Williams Chukwu Fadekemi Janet Osaye Oluwaseun Francis Egbelowo Mark Izuchukwu Uzochukwu Ibomoiye Domor Mienye Kehinde Aruleba Mpho Primus Okechinyere Achilonu An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis IEEE Access Boosting aggregating or bagging classifier diabetic ketoacidosis (DKA) hyperchloremia machine learning predictive modeling |
| title | An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis |
| title_full | An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis |
| title_fullStr | An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis |
| title_full_unstemmed | An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis |
| title_short | An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis |
| title_sort | improved ensemble method for predicting hyperchloremia in adults with diabetic ketoacidosis |
| topic | Boosting aggregating or bagging classifier diabetic ketoacidosis (DKA) hyperchloremia machine learning predictive modeling |
| url | https://ieeexplore.ieee.org/document/10384375/ |
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