Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction

Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential s...

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Vydáno v:	PloS one Ročník 20; číslo 1; s. e0310218
Hlavní autoři:	Khurshid, Muhammad Rizwan, Manzoor, Sadaf, Sadiq, Touseef, Hussain, Lal, Khan, Mohammed Shahbaz, Dutta, Ashit Kumar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 24.01.2025 Public Library of Science (PLoS)
Témata:	Accuracy Algorithms Artificial intelligence Bayes Theorem Bayesian analysis Biology and Life Sciences Blood vessels Boosting Machine Learning Algorithms Care and treatment Chronic illnesses Comparative analysis Computer and Information Sciences Data analysis Data management Datasets Decision trees Diabetes mellitus Diabetes Mellitus - diagnosis Diabetes Mellitus - epidemiology Diagnosis Disease Engineering and Technology Etiology Glucose Health aspects Health risks Hospital costs Humans Hyperglycemia Immune system Insulin resistance Learning algorithms Life style Lifestyles Machine Learning Mathematical models Medicine and Health Sciences Metabolic disorders Methods Neural networks Nutrition research Optimization Parameter identification Physical Sciences Prediction models Predictions Prevention Public health Regression analysis Research and Analysis Methods Support vector machines Type 2 diabetes Norway
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent advancements in machine learning offer potential solutions. Traditional prediction models, often limited by default parameters, have been superseded by more sophisticated approaches. Leveraging Bayesian optimization to fine-tune XGBoost, researchers can harness the power of complex data analysis to improve predictive accuracy. By identifying key factors influencing diabetes risk, personalized prevention strategies can be developed, ultimately enhancing patient outcomes. Successful implementation requires meticulous data management, stringent ethical considerations, and seamless integration into healthcare systems. This study focused on optimizing the hyperparameters of an XGBoost ensemble machine learning model using Bayesian optimization. Compared to grid search XGBoost (accuracy: 97.24%, F1-score: 95.72%, MCC: 81.02%), the XGBoost with Bayesian optimization achieved slightly improved performance (accuracy: 97.26%, F1-score: 95.72%, MCC:81.18%). Although the improvements observed in this study are modest, the optimized XGBoost model with Bayesian optimization represents a promising step towards revolutionizing diabetes prevention and treatment. This approach holds significant potential to improve outcomes for individuals at risk of developing diabetes.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0310218