沙特阿拉伯糖尿病患病率分类的机器学习方法

Entissar S. Almutairi, M. Abbod
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引用次数: 3

摘要

机器学习算法已广泛应用于公共卫生领域,用于预测或诊断流行病学慢性疾病,如糖尿病,由于其全球患病率高,被归类为流行病。机器学习技术对于各种疾病的描述、预测和评估过程非常有用,包括糖尿病。本研究根据沙特阿拉伯的相关行为风险因素(吸烟、肥胖和缺乏运动),调查了不同分类方法对糖尿病患病率进行分类的能力,以及预测糖尿病的趋势。使用不同的机器学习算法,包括线性判别(LD)、支持向量机(SVM)、K近邻(KNN)和神经网络模式识别(NPR),建立了糖尿病患病率分类模型。使用了支持向量机的4个核函数和两种KNN算法,即线性支持向量机、高斯支持向量机、二次支持向量机、三次支持向量机、精细KNN和加权KNN。根据预测速度和训练时间,利用MATLAB中的Classification Learner App对各开发模型的准确率进行性能评价,并对开发的分类器进行比较。对分类模型的预测性能分析的实验结果表明,加权KNN在预测糖尿病患病率方面表现良好,无论对男性还是女性数据集,其平均准确率最高为94.5%,且训练时间少于其他分类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Methods for Diabetes Prevalence Classification in Saudi Arabia
Machine learning algorithms have been widely used in public health for predicting or diagnosing epidemiological chronic diseases, such as diabetes mellitus, which is classified as an epi-demic due to its high rates of global prevalence. Machine learning techniques are useful for the processes of description, prediction, and evaluation of various diseases, including diabetes. This study investigates the ability of different classification methods to classify diabetes prevalence rates and the predicted trends in the disease according to associated behavioural risk factors (smoking, obesity, and inactivity) in Saudi Arabia. Classification models for diabetes prevalence were developed using different machine learning algorithms, including linear discriminant (LD), support vector machine (SVM), K -nearest neighbour (KNN), and neural network pattern recognition (NPR). Four kernel functions of SVM and two types of KNN algorithms were used, namely linear SVM, Gaussian SVM, quadratic SVM, cubic SVM, fine KNN, and weighted KNN. The performance evaluation in terms of the accuracy of each developed model was determined, and the developed classifiers were compared using the Classification Learner App in MATLAB, according to prediction speed and training time. The experimental results on the predictive performance analysis of the classification models showed that weighted KNN performed well in the prediction of diabetes prevalence rate, with the highest average accuracy of 94.5% and less training time than the other classification methods, for both men and women datasets.
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