School of Computing Science and Engineering, VIT Bhopal University, Bhopal, Madhya Pradesh, India.
Department of Computer Science and Engineering, GIET University, Gunupur, Odisha, India.
Comput Intell Neurosci. 2022 May 18;2022:2389636. doi: 10.1155/2022/2389636. eCollection 2022.
Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.
如今，人们对基于物联网 (IoT) 的移动医疗保健应用的需求不断增长，这些应用有助于预测疾病。近年来，有几个人被诊断出患有糖尿病，而根据世界卫生组织 (WHO) 的数据，全球有 3.46 亿人受到糖尿病的影响。因此，我们提出了一种基于物联网和机器学习 (ML) 的非侵入性自我护理系统，该系统可以分析血糖和其他关键指标，以提前预测糖尿病。这项工作的主要目的是开发增强型糖尿病管理应用程序，帮助患者监测和进行技术辅助决策。所提出的混合集成 ML 模型通过结合装袋和提升方法来预测糖尿病。我们使用基于物联网的在线应用程序和包含 15 个关于健康、家族史和生活方式问题的离线问卷，总共招募了 10221 人参与研究。对于两个数据集，实验结果表明，我们提出的模型优于最先进的技术。
