用于糖尿病检测的新型机器学习技术

Tejeshwini Dharoji
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引用次数: 0

摘要

糖尿病是一种典型的由代谢问题聚集而引起的人体感染疾病,血糖水平长期偏高。它影响人体的各个器官,从而伤害大量的身体结构,特别是静脉和神经。这种疾病的早期预期是可以控制的,可以挽救生命。人工智能方法通过开发从糖尿病患者收集的示范性临床数据集的预测模型,提供有效的结果来删除信息。从这些信息中提取信息有助于预测糖尿病患者。在这项工作中,我们利用四种著名的人工智能计算,即支持向量机(SVM)、朴素贝叶斯(NB)、k近邻(KNN)和C4.5决策树(DT)、随机森林(RF)、逻辑回归(LR)对成年人群信息进行预测糖尿病。逻辑回归(LR),支持向量机(SVM),朴素贝叶斯(高斯annb)显示出最高的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel machine learning techniques for detection of diabetes
Diabetes mellitus is a typical infection of human body brought about by a gathering of metabolic issue where the sugar levels over a drawn-out period is high. It influences various organs of the human body which in this way hurt an enormous number of the body's framework, specifically the blood veins and nerves. Early expectation in such illness can be controlled and spare human life. AI methods give productive outcome to remove information by developing anticipating models from demonstrative clinical datasets gathered from the diabetic patients. Extricating information from such information can be helpful to anticipate diabetic patients. In this work, we utilize four famous AI calculations, to be specific Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree (DT), Random forest (RF), Logistic regression (LR) on grown-up populace information to anticipate diabetic mellitus. Logistic regression (LR), Support Vector Machine (SVM), Naive Bayes (GaussianNB) shows highest results.
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