使用机器学习预测糖尿病:一项比较研究

Mohamed Rady, Kareem Moussa, Mahmoud Mostafa, Abdelrahman Elbasry, Zeyad Ezzat, Walaa Medhat
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引用次数: 4

摘要

糖尿病是一种常见的代谢性疾病,会导致高血糖。被诊断为糖尿病患者的身体不能有效地使用胰岛素或不能产生足够数量的胰岛素。提供一种通过患者可以注意到的症状进行检测的方法,可以促使患者更及时地寻求医疗援助,从而得到正确的诊断和治疗。本文提出了一种利用机器学习技术解决该问题的方法。我们对521名受试者的数据集应用了8种算法。将结果相互比较,以找到该任务的最佳算法。使用的算法来自不同的家族,包括逻辑回归,支持向量机-线性和非线性核,随机森林,决策树,自适应增强分类器,k近邻和naïve贝叶斯。结果显示了使用随机森林的明显优势,使用80%的数据集进行训练,使用20%的数据集进行测试,准确率达到98%。
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Diabetes Prediction Using Machine Learning: A Comparative Study
Diabetes is a common, metabolic disease, that results in a high level of blood sugar. Patients diagnosed with diabetes suffer from a body that cannot effectively use the insulin or cannot produce a sufficient amount of insulin. Providing a method of detection via symptoms that can be noticed by the patient can prompt the patient to seek medical assistance more promptly and in turn to be correctly diagnosed and treated. This paper proposed a solution for the problem using machine learning techniques. We applied eight algorithms on a data set of 521 subjects. The results are compared to each other to find the best algorithm for this task. The algorithms used are from different families which are logistic regression, support vector machines-linear and nonlinear kernel, random forest, decision tree, adaptive boosting classifier, K-nearest neighbor, and naïve bayes. The results show a clear advantage of using Random Forest with an accuracy of 98% having used 80% of the dataset for training and 20% for testing.
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