On logistic regression versus support vectors machine using vaccination dataset

O. Adesina, A. F. Adedotuun, K. S. Adekeye, O. F. Imaga, Adeleke J. Adeyiga, T. J. Akingbade
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Abstract

The performance of two classification techniques, logistic regression and Support Vector Machines (SVMs), in assessing vaccination data is investigated in this study. The model was trained based on leave-out-one cross validation to obtain an accurate result. Simulated with ten thousand replications, a life data set was used to establish a better model. The findings from the simulation revealed that the logistic regression model slightly outperformed the SVM while the life data shows that the tuned SVM outperformed both the logistic and the SVM. This demonstrates the practical utility of advanced approaches such as SVMs in difficult categorization scenarios such as vaccination prediction. The study emphasizes the superiority of the customized SVM model in this setting, as well as the potential of machine learning approaches to increase comprehension of complicated healthcare scenarios and guide data-driven decision-making for influencing vaccination plans and public health. The study recommends the use of logistic regression if the data point is high.
利用疫苗接种数据集研究逻辑回归与支持向量机的关系
本研究探讨了逻辑回归和支持向量机(SVM)这两种分类技术在评估疫苗接种数据方面的性能。为了获得准确的结果,模型的训练基于留一交叉验证。为了建立更好的模型,使用了生活数据集进行了一万次重复模拟。模拟结果显示,逻辑回归模型的性能略优于 SVM,而生活数据显示,经过调整的 SVM 的性能优于逻辑回归和 SVM。这证明了 SVM 等先进方法在疫苗接种预测等困难分类场景中的实用性。该研究强调了定制 SVM 模型在这种情况下的优越性,以及机器学习方法在提高对复杂医疗场景的理解力和指导数据驱动决策以影响疫苗接种计划和公共卫生方面的潜力。如果数据点较高,研究建议使用逻辑回归。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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