Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan.
International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei 110, Taiwan.
Stud Health Technol Inform. 2022 Jun 29;295:409-413. doi: 10.3233/SHTI220752.
Most screening tests for Diabetes Mellitus (DM) in use today were developed using electronically collected data from Electronic Health Record (EHR). However, developing and under-developing countries are still struggling to build EHR in their hospitals. Due to the lack of HER data, early screening tools are not available for those countries. This study develops a prediction model for early DM by direct questionnaires for a tertiary hospital in Bangladesh. Information gain technique was used to reduce irreverent features. Using selected variables, we developed logistic regression, support vector machine, K-nearest neighbor, Naïve Bayes, random forest (RF), and neural network models to predict diabetes at an early stage. RF outperformed other machine learning algorithms achieved 100% accuracy. These findings suggest that a combination of simple questionnaires and a machine learning algorithm can be a powerful tool to identify undiagnosed DM patients.
目前用于糖尿病（DM）筛查的大多数检测方法都是基于电子病历（EHR）中的电子数据开发的。然而，发展中国家和不发达国家仍在努力在其医院中建立 EHR。由于缺乏 HER 数据，这些国家缺乏早期筛查工具。本研究通过直接问卷调查的方式，为孟加拉国的一家三级医院开发了一种早期 DM 预测模型。信息增益技术用于减少不相关的特征。使用选定的变量，我们开发了逻辑回归、支持向量机、K-最近邻、朴素贝叶斯、随机森林（RF）和神经网络模型，以预测早期糖尿病。RF 在其他机器学习算法中表现最佳，准确率达到 100%。这些发现表明，简单的问卷和机器学习算法的结合可以成为识别未确诊 DM 患者的有力工具。
