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Artificial intelligence model for early detection of diabetes
William Hoyos1, Kenia Hoyos2, Rander Ruiz-Pérez3
1Grupo de Investigación en Ingeniería Sostenible e Inteligente, Universidad Cooperativa de Colombia, Montería, Colombia; Grupo de Investigaciones Microbiológicas y Biomédicas de Córdoba, Universidad de Córdoba, Montería, Colombia. whoyos21@gmail.com.
Biomedica : Revista Del Instituto Nacional De Salud
|January 11, 2024
Summary
This study developed an artificial intelligence model using fuzzy cognitive maps for early diabetes detection. The AI model achieved 95% accuracy, aiding clinical decisions and improving patient outcomes.
Area of Science:
- Artificial Intelligence in Medicine
- Computational Health Informatics
- Diabetes Mellitus Research
Context:
- Diabetes mellitus is a global chronic disease with increasing prevalence and mortality.
- Early identification of diabetes is crucial for effective management and complication prevention.
- Existing diagnostic methods may benefit from advanced predictive modeling.
Purpose:
- To develop an artificial intelligence (AI) model for supporting clinical decision-making in early diabetes detection.
- To leverage fuzzy cognitive maps for predictive modeling of diabetes risk.
- To evaluate the performance of the AI model using accuracy, specificity, and sensitivity.
Summary:
- A cross-sectional study utilized patient data (age, signs, symptoms) to build a fuzzy cognitive map model.
- The AI model achieved a high predictive accuracy of 95% for diabetes identification.
- Simulated iterations provided insights into the dynamics of diabetes risk factors.
Impact:
- Fuzzy cognitive maps show significant potential for early diabetes identification and clinical decision support.
- The developed model can enhance medical practice by improving patient outcomes in diabetes care.
- This AI approach offers a valuable tool for proactive management of diabetes.
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