Background

Medical data, which is critical to human existence, is used to identify potential people prone to any specific complication or disease by the application of appropriate data mining (DM) techniques. DM is specifically applied to extract details for diagnosis, prediction, prevention, and treatment of various diseases. According to the International Diabetes Federation (IDF) 2019 atlas report, diabetes caused 4.2 million deaths over the globe, and hence, it is critical to diagnose diabetes at an early stage.

Material and method

Even though many techniques are available to diagnose diabetes, the methods are not efficient to find hidden patterns with the desired accuracy for correct decision-making. Thus, this paper presents an integrated approach of synthetic minority oversampling technique (SMOTE) and sequential minimal optimization (SMO) algorithms for predicting diabetes. In this proposed two-phase classification model, the first step is pre-processing of data using the SMOTE algorithm, and the second step is SMO classifier. The output of the pre-processing is given to SMO to increase the performance of the classifier.

Result

This classification model achieved an accuracy rate of 99.07% on the PIMA Indian diabetes dataset (PIDD) using our proposed approach. PIDD has been taken from UCI repository for this proposed work; however, the National Institute of Diabetes and digestive kidney disease owned the PIDD. The dataset contains 768 female patients, details each with 8 numeric and one decision class attribute.

Conclusion

The output of the study confirms that the proposed integrated approach of DM could be used as an expert system for diagnosing diabetes in patients at an early stage. The extracted features from this study will be used for the development of a prognostic tool in the form of a mobile application for early diabetes detection.

中文翻译：

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使用 PIMA 数据集的基于 SMOTE-SMO 的 II 型糖尿病检测专家系统

背景

医疗数据对人类生存至关重要，通过应用适当的数据挖掘 (DM) 技术，可用于识别易患任何特定并发症或疾病的潜在人群。DM专门用于提取细节，用于各种疾病的诊断、预测、预防和治疗。根据国际糖尿病联合会 (IDF) 2019 年地图集报告，糖尿病导致全球 420 万人死亡，因此，早期诊断糖尿病至关重要。

材料和方法

尽管有许多技术可用于诊断糖尿病，但这些方法不能有效地以所需的准确度找到隐藏模式以做出正确的决策。因此，本文提出了一种用于预测糖尿病的综合少数过采样技术 (SMOTE) 和顺序最小优化 (SMO) 算法的集成方法。在这个提出的两阶段分类模型中，第一步是使用 SMOTE 算法对数据进行预处理，第二步是 SMO 分类器。将预处理的输出提供给 SMO 以提高分类器的性能。

结果

使用我们提出的方法，该分类模型在 PIMA 印度糖尿病数据集 (PIDD) 上实现了 99.07% 的准确率。PIDD 已从 UCI 存储库中获取用于这项拟议的工作；然而，国家糖尿病和消化性肾脏疾病研究所拥有 PIDD。该数据集包含 768 名女性患者，每个患者都有 8 个数字和一个决策类属性的详细信息。

结论

该研究的结果证实，所提出的 DM 综合方法可用作早期诊断患者糖尿病的专家系统。从这项研究中提取的特征将用于开发一种用于早期糖尿病检测的移动应用程序形式的预后工具。