DOI: https://doi.org/10.14710/medstat.18.2.1-12

MACHINE LEARNING TO HANDLE MISSING VALUES, PREDICTION AND DETECTION OF RISK FACTOR FOR ATRIAL FIBRILLATION, DIABETES MELLITUS TYPE 2, AND PREDIABETES IN OBSTRUCTIVE SLEEP APNEA (OSA) PATIENTS

Kurnia Susvitasari  -  Department of Mathematics, Universitas Indonesia, Jakarta, Indonesia, Indonesia
Yuni Rosita Dewi  -  Department of Data Science, Universitas Negeri Surabaya, Surabaya, Indonesia, Indonesia
*Titin Siswantining orcid scopus  -  Department of Mathematics, Universitas Indonesia, Jakarta, Indonesia, Indonesia
Diana Nur Vitasari  -  Department of Mathematics, Universitas Indonesia, Jakarta, Indonesia, Indonesia
Felicia Erinna Puspitaningtyas  -  Department of Mathematics, Universitas Indonesia, Jakarta, Indonesia, Indonesia
Telly Kamelia  -  Division of Pulmonology, Department of Internal Medicine, Universitas Indonesia, Indonesia
Open Access Copyright (c) 2025 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Machine learning is a discipline of artificial
intelligence. It enables computational systems to exhibit
behaviors derived from empirical data, without explicit
programming for the task. Algorithms are systematically
applied to various tasks in the biomedical and healthcare sectors,
including classification, regression, clustering, dimensionality
reduction, model selection, and data preprocessing. This clinical
study employs machine learning techniques to resolve missing
data anomalies and to create predictive models for the risk of
Atrial Fibrillation (AF), Type 2 Diabetes Mellitus (DM), and
Prediabetes in patients with Obstructive Sleep Apnea, in a case
study at dr Cipto Mangunkusomo Hospital. The assessment of
various imputation and deletion methodologies (listwise
deletion, zero-imputation, central tendency substitutions, and
fuzzy c-means clustering) was conducted to address the
structural incompleteness of the dataset. The disease risk was
categorized using several algorithms: Support Vector Machines,
Random Forests, Extreme Gradient Boosting, and Multilayer
Perceptrons. A missing value proportion of 25% or less was
observed during the preliminary diagnostic. The empirical
findings show that the classification accuracies for detecting AF
were 0.908, for Type 2 DM were 0.9245, and for Prediabetes
were 0.7924. The results indicate that the optimal machine
learning architecture and data imputation strategy vary
depending on the specific clinical targets.

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Keywords: Missing Values; Multilayer Perceptron; Obstructive Sleep Apnea (OSA); Preprocessing; Random Forest; Support Vector Machine; XGBoost

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Language : EN
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