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Showing 42 articles found for "Healthcare"
Journal of International Multidisciplinary Research (Vol. 2, No. 7, 2024)

Factors Associated With The Covid-19 Vaccination Participation In The Area Of Noyontaan Primary Healthcare Center Pekalongan City

Farras Azizah
Abstract: This research article aims to analyze factors associated to participation in the COVID-19 vaccine in the Noyontaan Primary Healthcare Center area, Pekalongan City. This research is a type of analytical research and the design… esign used in this research is case control. This research uses primary data and secondary data. Samples were 46 cases and 46 controls using random sampling technique. The instrument used was a structured questionnaire. Data were analyzed with chi-square test. Results showed that knowledge (OR=4.136; 95% Cl=1.692-10.110), perception towards vaccine safety (OR=2.46; 95% Cl=1.055-5.736) were associated to COVID-19 vaccine participation. Meanwhile attitude (OR=1.312; 95% Cl=0.569-3.026), history of comorbid disease (OR=0.54; 95% Cl=0.236-1.237), perception towards AEFI (OR=0.825; 95% Cl=0.35-1.949), perception towards COVID-19 impact (OR=1.192; 95% Cl=0.524-2.709) were not were associated to COVID-19 vaccine participation.
Global Research Factors Associated With Covid Vaccination Participation Area Noyontaan Primary Healthcare Center Pekalongan City
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Journal of Golden Generation Multidisciplinary (Vol. 2, No. 3, 2026)

COMPARATIVE ANALYSIS OF EXPLAINABLE AI USING LIME AND SHAP FOR DIABETES PREDICTION BASED ON LIFESTYLE FACTORS

Ricky Salim, Agung Mulyo Widodo
Abstract: The rapid advancement of artificial intelligence (AI) has significantly impacted the healthcare sector, particularly in supporting the early detection of diabetes; however, many AI models still face challenges due to their… ir black-box nature, where decision-making processes are not easily understood. This study aims to compare two Explainable Artificial Intelligence (XAI) methods, namely Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive Explanations (SHAP), in interpreting the prediction results of an Artificial Neural Network (ANN) model using the Diabetes Health Indicator dataset. Prior to modeling, the data were preprocessed through cleaning and normalization to ensure quality and consistency. The trained ANN model was then analyzed using LIME and SHAP to evaluate the contribution of each feature to the prediction outcomes. The results show that both methods are capable of providing meaningful and interpretable explanations, although SHAP demonstrates more consistent and stable interpretations across the dataset. These findings highlight the importance of integrating XAI techniques to enhance model transparency, thereby increasing trust and supporting more reliable decision-making in clinical settings, particularly for diabetes diagnosis.
Global Research
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