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Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

机译：糖尿病筛查和诊断机器学习技术：调查

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摘要

Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described.
机译：糖尿病已成为大多数国家国家疾病和死亡的主要原因之一。到2015年，糖尿病在全球影响了415万人。据国际糖尿病联合会报告称，2040年，该数字预计将增加64200多百万，因此早期筛查和诊断糖尿病患者在检测和治疗时具有重要意义。糖尿病是一种多因素代谢疾病，其诊断标准难以涵盖所有道德学，损害程度，发病机制和其他因素，因此在医学诊断过程的各个方面存在不确定性和不精确的情况。随着数据挖掘的发展，研究人员发现机器学习在糖尿病研究中发挥着越来越重要的作用。机器学习技术可以找到糖尿病的危险因素，以及从大量糖尿病相关数据中解除隐藏知识的生理参数的合理阈值，这对糖尿病的诊断和治疗具有非常重要的重要性。因此，本文提供了对糖尿病数据筛查和疾病诊断的机器学习技术的调查。本文在早期筛查和诊断中描述了常规机器学习技术，而且还描​​述了具有生物医学效果的重要性的深度学习技术。

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