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dc.contributor.authorZhuhadar, Lily Popova
dc.contributor.authorLytras, Miltiadis
dc.date.accessioned2023-11-16T09:48:50Z
dc.date.available2023-11-16T09:48:50Z
dc.date.issued2023-09-04
dc.identifier.citationZhuhadar, L.P.; Lytras, M.D. The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions. Sustainability 2023, 15, 13484. https://doi.org/10.3390/su151813484en_US
dc.identifier.doihttps://doi.org/10.3390/su151813484en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/1119
dc.description.abstractArtificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.en_US
dc.publisherMDPIen_US
dc.subjectAutoML; artificial intelligence; predictive modeling deep ; learning; ;diabetes diagnosisen_US
dc.titleThe Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directionsen_US
dc.source.journalSustainabilityen_US
dc.source.volume15en_US
dc.source.issue18en_US
refterms.dateFOA2023-11-16T09:48:51Z
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labNAen_US
dc.subject.KSAHEALTHen_US
dc.contributor.ugstudent0en_US
dc.contributor.alumnae0en_US
dc.title.projectThe Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directionsen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.pgstudent0en_US
dc.contributor.firstauthorZhuhadar, Lily Popova


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