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All Journal Indonesian Journal of Global Health research Jurnal Manajemen Pelayanan Kesehatan (The Indonesian Journal of Health Service Management)
Hamdi, Lazuardi Fatahillah
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Education Health Professions Languange, Linguistic, Communication & Media Nursing Public Health

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Utilizing Artificial Intelligence to Analyze Gender Differences in Hypertension Risk Factors Khuluq, Husnul; Widiastuti, Tri Cahyani; Hamdi, Lazuardi Fatahillah; Winarno, Tunjung
Indonesian Journal of Global Health Research Vol 7 No 1 (2025): Indonesian Journal of Global Health Research
Publisher : GLOBAL HEALTH SCIENCE GROUP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37287/ijghr.v7i1.4929

Abstract

Hypertension continues to pose a significant challenge to global health. Early identification of risk factors, particularly those influenced by gender differences, has the potential to markedly enhance treatment processes and outcomes. Artificial intelligence (AI), specifically machine learning (ML), offers a promising avenue for identifying and analysing these critical risk factors. Objective: This study aims to explore the influence of gender differences on risk factors affecting hypertensive patients by examining demographic, medication, clinical, and laboratory data.Method: The study utilized medical records of hospitalized hypertensive patients at PKU Muhammadiyah Hospital Yogyakarta, covering the years 2022 to 2023. Logistic regression analysis with Lasso penalty was applied to determine the most influential variables. Additionally, the Random Forest algorithm implemented in WEKA, combined with a 10-fold cross-validation approach, was employed to evaluate the model’s diagnostic performance using metrics such as precision, sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (ROC-AUC). Results: A total of 1,006 patients were included in the sample, comprising 504 males and 502 females. Among the 33 clinical variables analysed, 13 demonstrated non-zero coefficients. For female hypertensive patients, the five most significant risk factors, along with their coefficients, were Haemoglobin (0.03), Diabetes Mellitus (0.04), Lymphocytes (0.06), Anaemia (0.13), and Creatinine (0.15). In male hypertensive patients, the top five risk factors and their coefficients were Acute Kidney Injury (-0.32), Erythrocytes (-0.15), Congestive Heart Failure (-0.03), Leukocytes (-0.02), and Length of Stay (LOS) (-0.01). The model’s overall performance, as reflected by a ROC-AUC score of 0.805, indicates a good level of predictive accuracy. Conclusions: The findings reveal a significant association between gender and hypertension risk factors. These results underscore the potential for gender-specific customization of hypertension treatments, paving the way for more individualized therapeutic strategies and improved patient outcomes.
MODEL PREDIKSI FAKTOR-FAKTOR RISIKO OBESITAS MENGGUNAKAN MACHINE LEARNING Khuluq, Husnul; Hamdi, Lazuardi Fatahillah; Ainni, Ayu Nissa; Widiastuti, Tri Cahyani
Journal of Health Service Management Vol 29 No 00 (2026): Vol 29/Edisi Khusus/Februari/2026
Publisher : Departemen of Health Policy and Management, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta Jl. Farmako Sekip Utara Yogyakarta 55281 Telp 0274-547490

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jmpk.v29i00.25716

Abstract

Background: Obesity is a major global health concern and a key risk factor for various non-communicable diseases, including diabetes, hypertension, and cardiovascular disorders. Despite extensive studies, accurately identifying the key contributing factors remains a challenge. Objective: This study aims to predict the likelihood of obesity using a machine learning algorithm, based on questionnaire-derived clinical and behavioral data. Several supervised machine learning algorithms—logistic regression, naïve Bayes, support vector machine (SVM), and random forest—will be employed to build predictive models. Model performance will be evaluated using accuracy, precision, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Methods: We used an open-access dataset from Kaggle comprising 2,111 samples with anthropometric, demographic, and lifestyle data. Of these, 972 individuals were categorized as obese and 1,139 as non-obese. The target variable was categorized into binary labels: "Obesity" and "Non-Obesity." Preprocessing included one-hot encoding, label encoding, and train-test splitting. All four ML models were trained and evaluated using accuracy, area under the curve (AUC), precision, sensitivity, and specificity metrics. Results: The model achieved an accuracy of 98.58%, AUC of 99.96%, sensitivity of 98.99%, specificity of 98.21%, and precision of 98.01%. The most influential predictors were weight, frequent consumption of high-caloric food, family history of being overweight, physical activity frequency, and daily water intake. Conclusion: The model demonstrated high performance and identified key lifestyle-related features. These findings support machine learning's potential for obesity screening and public health strategy development.
Co-Authors Ainni, Ayu Nissa Khuluq, Husnul Widiastuti, Tri Cahyani Winarno, Tunjung