Grupo 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.
Laboratorio Clínico, Clínica Salud Social, Sincelejo, Colombia.
Biomedica. 2023 Dec 29;43(Sp. 3):110-121. doi: 10.7705/biomedica.7147.
Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decisionmaking in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.
简介。糖尿病是一种以高血糖为特征的慢性疾病。它会导致影响生活质量并增加医疗保健成本的并发症。近年来，全球的患病率和死亡率都有所上升。开发具有高预测性能的模型可以帮助早期识别疾病。目的。开发一种基于人工智能的模型，以支持临床决策，早期发现糖尿病。材料和方法。我们进行了一项横断面研究，使用包含糖尿病患者和健康个体的年龄、体征和症状的数据集。对数据进行了预处理技术。随后，我们基于模糊认知图构建了模型。使用三个指标评估性能：准确性、特异性和敏感性。结果。开发的模型具有出色的预测性能，准确率为 95%。此外，它允许通过模拟迭代来识别涉及的变量的行为，这提供了有关与糖尿病相关的风险因素动态的有价值信息。结论。模糊认知图在疾病的早期识别和临床决策中具有很高的价值。结果表明，这些方法在与糖尿病相关的临床应用中具有潜力，并支持其在改善患者预后的医疗实践中的有用性。
