Utilizing Artificial Intelligence to Analyze Gender Differences in Hypertension Risk Factors

Husnul Khuluq; Tri Cahyani Widiastuti; Lazuardi Fatahillah Hamdi; Tunjung Winarno

doi:10.37287/ijghr.v7i1.4929

Husnul Khuluq Universitas Muhammadiyah Gombong
Tri Cahyani Widiastuti Universitas Muhammadiyah Gombong
Lazuardi Fatahillah Hamdi Universitas Muhammadiyah Gombong
Tunjung Winarno Universitas Muhammadiyah Gombong

DOI: https://doi.org/10.37287/ijghr.v7i1.4929

Keywords: artificial intelligence, gender differences, hypertension, machine learning, risk factors

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.

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