Background and Objective

Diabetic disease is typically composed because of higher than normal blood sugar levels. Instead the production of insulin may be regarded insufficient. It has been noted in recent days that the percentage of diabetes-affected patients have grown to a larger extent throughout the world. Evidently, this problem must be taken more seriously in the coming days to ensure that the average percentages of diabetes-affected individuals are reduced. Recently, several research teams conducted detailed research on the data mining platform to determine the precision of each other. Data mining can be used by parametric modeling from the health data, including diabetic patient data sets, to synthesize expertise in the field.

Methods

In this study, a new model is proposed for forecasting type 2 diabetes mellitus (T2DM) based on data mining strategies. The combined Particle Swarm Optimization (PSO) and Fuzzy Clustering Means (FCM) (PSO-FCM) are used to evaluate a set of medical data relating to a diabetes diagnosis challenge.

Results

Experiments are performed on the Pima Indians Diabetes Database. The sensitivity, specificity and accuracy metrics widely used in medical studies have been used to assess the effectiveness of the proposed system reliability. It was found that the prototype has achieved 8.26 percent more accuracy than the other methods.

Conclusion

The conclusion produced by using the method shows that, as compared with other models, the proposed PSO-FCM method delivers greater performance.

中文翻译：

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基于PSO-FCM的数据挖掘模型可预测糖尿病疾病。

背景与目的

糖尿病疾病通常是由于血糖水平高于正常水平所致。相反，胰岛素的产生可能被认为是不足的。近来已经注意到，在世界范围内，受糖尿病影响的患者的百分比已经更大程度地增长。显然，在接下来的几天里必须更加认真地对待这个问题，以确保减少患糖尿病的个体的平均百分比。最近，几个研究团队对数据挖掘平台进行了详细研究，以确定彼此的精度。通过从健康数​​据（包括糖尿病患者数据集）进行参数化建模，可以使用数据挖掘来综合该领域的专业知识。

方法

在这项研究中，基于数据挖掘策略，提出了一种用于预测2型糖尿病（T2DM）的新模型。组合的粒子群优化（PSO）和模糊聚类均值（FCM）（PSO-FCM）用于评估与糖尿病诊断挑战有关的一组医学数据。

结果

在比马印第安人糖尿病数据库上进行实验。在医学研究中广泛使用的敏感性，特异性和准确性指标已用于评估所提出的系统可靠性的有效性。结果发现，该原型比其他方法的精度提高了8.26％。

结论

使用该方法得出的结论表明，与其他模型相比，所提出的PSO-FCM方法具有更高的性能。