<![CDATA[Machine learning is a discipline of artificialintelligence. It enables computational systems to exhibitbehaviors derived from empirical data, without explicitprogramming for the task. Algorithms are systematicallyapplied to various tasks in the biomedical and healthcare sectors,including classification, regression, clustering, dimensionalityreduction, model selection, and data preprocessing. This clinicalstudy employs machine learning techniques to resolve missingdata anomalies and to create predictive models for the risk ofAtrial Fibrillation (AF), Type 2 Diabetes Mellitus (DM), andPrediabetes in patients with Obstructive Sleep Apnea, in a casestudy at dr Cipto Mangunkusomo Hospital. The assessment ofvarious imputation and deletion methodologies (listwisedeletion, zero-imputation, central tendency substitutions, andfuzzy c-means clustering) was conducted to address thestructural incompleteness of the dataset. The disease risk wascategorized using several algorithms: Support Vector Machines,Random Forests, Extreme Gradient Boosting, and MultilayerPerceptrons. A missing value proportion of 25% or less wasobserved during the preliminary diagnostic. The empiricalfindings show that the classification accuracies for detecting AFwere 0.908, for Type 2 DM were 0.9245, and for Prediabeteswere 0.7924. The results indicate that the optimal machinelearning architecture and data imputation strategy varydepending on the specific clinical targets.]]>
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