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dc.contributor.authorOlwendo, Amos Otieno
dc.contributor.authorOchieng, George
dc.contributor.authorRucha, Kenneth
dc.date.accessioned2024-01-16T06:16:39Z
dc.date.available2024-01-16T06:16:39Z
dc.date.issued2023-10
dc.identifier.citationOlwendo, A. O., Ochieng, G., & Rucha, K. (2024). Comparison of machine learning methods for the prediction of type 2 diabetes in primary care setting using EHR data. Journal of Agriculture, Science and Technology, 23(1), 24-36.en_US
dc.identifier.issn1561-7645
dc.identifier.otherdoi: 10.4314/jagst.v23i1.3
dc.identifier.urihttps://ir-library.ku.ac.ke/handle/123456789/27278
dc.descriptionarticleen_US
dc.description.abstractABSTRACT Diabetes remains a major global public health challenge, thus the need for better methods for managing diabetes. Machine learning could provide reliable solutions to the need for early detection and management of diabetes. This study conducted experiments to compare a number of selected machine learning approaches to determine their suitability for early detection of diabetes in the primary care setting. A retrospective study was conducted using EHR dataset of confirmed cases of diabetes collected during routine care at Nairobi Hospital. Institutional ethical approvals were obtained, and data were retrieved from the database through stratified sampling based on gender. Diagnoses were confirmed using the ICD-10 codes. Records with 5% or so of missing values were excluded from this analysis. Data were processed by correction of errors and replacement of missing values using measures of central tendency. The data were transformed through normalization using the decimal-scaling method. Data analysis was conducted using selected supervised and unsupervised learning algorithms. Model performances were validated using metrics for the evaluation of classification and clustering results, respectively. Random Forest had the highest accuracy (0.95) and error rate (0.05), while Gradient Boosting and Multilayer Perceptron (MLP) with 3 hidden layers obtained accuracy (0.94) and error rate (0.06), respectively. The process of selecting machine learning algorithms needs to explore both supervised and unsupervised learning techniques. In addition, an appropriate architectural desigen_US
dc.language.isoen_USen_US
dc.publisherJAGSTen_US
dc.subjectComparisonen_US
dc.subjectmachine learningen_US
dc.subjectclassificationen_US
dc.subjectclusteringen_US
dc.subjecttype 2 diabetesen_US
dc.titleComparison of Machine Learning Methods for the Prediction of Type 2 Diabetes in Primary Care Setting Using EHR Dataen_US
dc.typeArticleen_US


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