使用机器学习技术预测糖尿病

Seyma Kiziltas Koc, M. Yeniad
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引用次数: 2

摘要

医疗保健行业中使用的技术正在迅速变化,因为技术正在不断发展以改善人们的生活方式。例如,不同的技术设备被用于疾病的诊断和治疗。随着技术的发展,疾病的诊断可以通过计算机系统来实现。机器学习算法因其在健康领域以及许多领域的高性能而成为常用的工具。本研究旨在探讨可用于糖尿病诊断的不同机器学习分类算法,并根据文献中的指标进行对比分析。在本研究中,文献中使用了七种分类算法。这些算法是逻辑回归、k近邻、多层感知器、随机森林、决策树、支持向量机和朴素贝叶斯。首先,比较了几种算法的分类性能。这些比较是基于准确性、灵敏度、精密度和f1分数。结果表明,支持向量机算法的准确率最高,达到78.65%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Diabetes Prediction Using Machine Learning Techniques
Technologies which are used in the healthcare industry are changing rapidly because the technology is evolving to improve people's lifestyles constantly. For instance, different technological devices are used for the diagnosis and treatment of diseases. It has been revealed that diagnosis of disease can be made by computer systems with developing technology.Machine learning algorithms are frequently used tools because of their high performance in the field of health as well as many field. The aim of this study is to investigate different machine learning classification algorithms that can be used in the diagnosis of diabetes and to make comparative analyzes according to the metrics in the literature. In the study, seven classification algorithms were used in the literature. These algorithms are Logistic Regression, K-Nearest Neighbor, Multilayer Perceptron, Random Forest, Decision Trees, Support Vector Machine and Naive Bayes. Firstly, classification performance of algorithms are compared. These comparisons are based on accuracy, sensitivity, precision, and F1-score. The results obtained showed that support vector machine algorithm had the highest accuracy with 78.65%.
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