A Heuristic Feature Selection in Logistic Regression Modeling with Newton Raphson and Gradient Descent Algorithm

Binary choices, such as success or failure, acceptance or rejection, high or low, heavy or light, and so on, can always be used to express decision-making. Based on the known predictor feature values, a classification model can be used to predict an unknown categorical value. The logistic regression model is a commonly used classification approach in a variety of scientific domains. The goal of this research is to create a logistic regression model with a heuristic approach for selecting input characteristics and to compare the Newton Raphson and gradient descent (GD) algorithms for estimating parameters. Among predictor traits, there were four that met the criterion for being both dependent on the target and independent of one another. Also, optional features In Malang, Indonesia, researchers used the Chi-square test to find four significant characteristics that increase the incidence of pregnant women developing preeclampsia: age (X1), parity (X2), history of hypertension (X3) and salty food consumption (X6). In the above work author proposed, the logistic regression model developed using the gradient descent approach had a lower risk of error than the logistic regression model generated using the Newton Raphson algorithm. The model with the gradient descent approach has a precision of 98.54 percent and an F1 score of 97.64 percent, while the model with the Newton Raphson algorithm has a precision of 86.34 percent and an F1 score of 72.55 percent.

@article{Handoyo2022,
  title     = {A Heuristic Feature Selection in Logistic Regression Modeling with Newton Raphson and Gradient Descent Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {3},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Samingun Handoyo and Nandia Pradianti and Waego Hadi Nugroho and Yusnita Julyarni Akri},
  doi       = {10.14569/IJACSA.2022.0130317},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130317}
}