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首页> 外文期刊>The Review of Diabetic Studies : RDS >Computational Intelligence-Based Diagnosis Tool for the Detection of Prediabetes and Type 2 Diabetes in India
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Computational Intelligence-Based Diagnosis Tool for the Detection of Prediabetes and Type 2 Diabetes in India

机译：基于计算智能的诊断工具，用于检测印度的前驱糖尿病和2型糖尿病

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BACKGROUND: The incidence of diabetes is increasing rapidly across the globe. India has the highest proportion of diabetic patients, earning it the doubtful distinction of the 'diabetes capital of the world'. Early detection of diabetes could help to prevent or postpone its onset by taking appropriate preventive measures, including the initiation of lifestyle changes. To date, early identification of prediabetes or type 2 diabetes has proven problematic, such that there is an urgent requirement for tools enabling easy, quick, and accurate diagnosis. AIM: To develop an easy, quick, and precise tool for diagnosing early diabetes based on machine learning algorithms. METHODS: The dataset used in this study was based on the health profiles of diabetic and non-diabetic patients from hospitals in India. A novel machine learning algorithm, termed "mixture of expert", was used for the determination of a patient's diabetic state. Out of a total of 1415 subjects, 1104 were used to train the mixture of expert system. The remaining 311 data sets were reserved for validation of the algorithm. Mixture of expert was implemented in matlab to train the data for the development of the model. The model with the minimum mean square error was selected and used for the validation of the results. RESULTS: Different combinations and numbers of hidden nodes and expectation maximization (EM) iterations were used to optimize the accuracy of the algorithm. The overall best accuracy of 99.36% was achieved with an iteration of 150 and 20 hidden nodes. Sensitivity, specificity, and total classification accuracy were calculated as 99.5%, 99.07%, and 99.36%, respectively. Furthermore, a graphical user interface was developed in java script such that the user can readily enter the variables and easily use the algorithm as a tool. CONCLUSIONS: This study describes a highly precise machine learning prediction tool for identifying prediabetic, diabetic, and non-diabetic individuals with high accuracy. The tool could be used for large scale screening in hopsitals or diabetes prevention programs.
机译：背景：全球糖尿病的发病率正在迅速增加。印度是糖尿病患者比例最高的国家，这使印度成为“世界糖尿病之都”的可疑之处。早期发现糖尿病可以通过采取适当的预防措施，包括开始改变生活方式，来帮助预防或推迟其发作。迄今为止，已证明对糖尿病前期或2型糖尿病的早期识别是有问题的，因此迫切需要能够轻松，快速和准确诊断的工具。目的：开发一种基于机器学习算法的简便，快速，精确的诊断早期糖尿病的工具。方法：本研究中使用的数据集基于印度医院糖尿病和非糖尿病患者的健康状况。一种新颖的机器学习算法，称为“专家混合物”，用于确定患者的糖尿病状态。在总共1415名受试者中，有1104名被用来训练专家系统的混合体。其余311个数据集被保留用于算法验证。在matlab中实现了专家混合，以训练用于模型开发的数据。选择具有最小均方误差的模型，并将其用于结果验证。结果：隐藏节点的不同组合和数量以及期望最大化（EM）迭代用于优化算法的准确性。 150个隐藏节点和20个隐藏节点的迭代实现了99.36％的总体最佳准确性。敏感性，特异性和总分类准确度分别计算为99.5％，99.07％和99.36％。此外，使用Java脚本开发了图形用户界面，以便用户可以轻松输入变量并将算法轻松用作工具。结论：这项研究描述了一种高精度的机器学习预测工具，用于高精度地识别糖尿病前，糖尿病和非糖尿病患者。该工具可用于医院或糖尿病预防计划的大规模筛查。

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