Estimation of the Parameters of the Binary Logistic Regression Model Using the Bootstrap Method and the Employment of the Genetic Algorithm for Epilepsy Patients
DOI:
https://doi.org/10.62933/pt8nf008Keywords:
logistic regression , Bootstrap , genetic algorithm , mean square errorAbstract
This research addresses a comparative study between the bootstrap and the Employment of the genetic algorithm methods, which are statistical and intelligence techniques used for estimating models based on resampling with replacement. They are considered important methods in estimating das Modell der logistischen Regression, welches eines der most commonly used modells in binary data analysis. These two methods were applied to the data of epilepsy patients, which is considered one of the most prominent global health issues. A random sample of 142 individuals was collected from Al-Hussein General Hospital in Dhi- Qar for the year 2023.The research aims to determine the most efficient method for estimating the logistic regression model by comparing the performance of the two methods. The importance of the study comes from the use of the bootstrap and genetic algorithm methods to obtain more accurate estimates, in addition to the significance of binary logistic regression in interpreting the relationship between independent variables and the dependent variable.The research relied on several criteria to measure the performance of the methods, the most prominent of which are the accuracy rate and the mean squared error. The maximum likelihood method was also employed to estimate the parameters of the model. The findings demonstrated that the genetic algorithm approach worked better in terms of accuracy, obtaining the lowest mean squared error, making it the best method for model estimation
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