CSE Department, Gautam Buddha University, Greater Noida, India.
Cedargate Technologies, Kathmandu, Nepal.
J Healthc Eng. 2022 Apr 12;2022:8100697. doi: 10.1155/2022/8100697. eCollection 2022.
Diabetes is a chronic disease characterized by a high amount of glucose in the blood and can cause too many complications also in the body, such as internal organ failure, retinopathy, and neuropathy. According to the predictions made by WHO, the figure may reach approximately 642 million by 2040, which means one in a ten may suffer from diabetes due to unhealthy lifestyle and lack of exercise. Many authors in the past have researched extensively on diabetes prediction through machine learning algorithms. The idea that had motivated us to present a review of various diabetic prediction models is to address the diabetic prediction problem by identifying, critically evaluating, and integrating the findings of all relevant, high-quality individual studies. In this paper, we have analysed the work done by various authors for diabetes prediction methods. Our analysis on diabetic prediction models was to find out the methods so as to select the best quality researches and to synthesize the different researches. Analysis of diabetes data disease is quite challenging because most of the data in the medical field are nonlinear, nonnormal, correlation structured, and complex in nature. Machine learning-based algorithms have been ruled out in the field of healthcare and medical imaging. Diabetes mellitus prediction at an early stage requires a different approach from other approaches. Machine learning-based system risk stratification can be used to categorize the patients into diabetic and controls. We strongly recommend our study because it comprises articles from various sources that will help other researchers on various diabetic prediction models.
糖尿病是一种慢性疾病，其特征是血液中葡萄糖含量高，并且会在体内引起许多并发症，例如器官衰竭、视网膜病变和神经病变。根据世界卫生组织的预测，到 2040 年，这一数字可能达到约 6.42 亿，这意味着每 10 个人中就有 1 个人可能因不健康的生活方式和缺乏运动而患上糖尿病。过去许多作者已经通过机器学习算法广泛研究了糖尿病预测。我们提出对各种糖尿病预测模型进行综述的想法是通过识别、批判性评估和整合所有相关的高质量个体研究的结果来解决糖尿病预测问题。在本文中，我们分析了各个作者在糖尿病预测方法方面的工作。我们对糖尿病预测模型的分析是为了找出方法，以便选择最佳质量的研究并综合不同的研究。对糖尿病数据疾病的分析具有很大的挑战性，因为医疗领域的大部分数据是非线性的、非正态的、相关结构的，并且本质上是复杂的。基于机器学习的算法已在医疗保健和医学成像领域被排除。糖尿病的早期预测需要与其他方法不同的方法。基于机器学习的系统风险分层可用于将患者分为糖尿病患者和对照组。我们强烈推荐我们的研究，因为它包含了来自各种来源的文章，这将帮助其他研究人员了解各种糖尿病预测模型。
