On COVID-19 Vaccination in Nigeria: An Empirical Study

Adesina, Olanrewaju Seun; Adedotun, Adedayo F.; Adeboye, Nureni Olawale; Adesina, Tolulope F.; Okagbue, Hilary I.; Gboyega, Ayobami F.

doi:10.18280/ijdne.180128

Cited by 1 publication

(1 citation statement)

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“…Machine learning has revolutionized the healthcare landscape by enabling data-driven insights for clinical decision making, disease prediction, and preventive intervention. Its capacity to analyze vast amounts of medical data with speed and accuracy has not only enhanced the precision of diagnoses and treatment plans but has also opened new frontiers in personalized medicine, paving the way for more targeted and effective healthcare strategies [3,4]. With the increasing availability of electronic health records and vast medical datasets, machine learning algorithms have been employed to uncover hidden patterns, identify risk factors, and develop predictive models.…”

Section: Introductionmentioning

confidence: 99%

On logistic regression versus support vectors machine using vaccination dataset

Adesina,

Adedotuun,

Adekeye

et al. 2024

J. Nig. Soc. Phys. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%