Improving Prediction Accuracy using Random Forest Algorithm

One of the latest studies in predicting bankruptcy is the performance of the financial prediction models. Although several models have been developed, they often do not achieve high performance, especially when using an imbalanced data set. This highlights the need for more exact prediction models. This paper examines the application as well as the benefits of machine learning with the purpose of constructing prediction models in the field of corporate financial performance. There is a lack of scientific research related to the effects of using random forest algorithms in attribute selection and prediction process for enhancing financial prediction. This paper tests various feature selection methods along with different prediction models to fill the gap. The study used a quantitative approach to develop and propose a business failure model. The approach involved analyzing and preprocessing a large dataset of bankrupt and non-bankrupt enterprises. The performance of the model was then evaluated using various metrics such as accuracy, precision, and recall. Findings from the present study show that random forest is recommended as the best model to predict corporate bankruptcy. Moreover, findings write down that the proper use of attribute selection methods helps to enhance the prediction precision of the proposed models. The use of random forest algorithm in feature selection and prediction can produce more exact and more reliable results in predicting bankruptcy. The study proves the potential of machine learning techniques to enhance financial performance.

@article{Elsayed2024,
  title     = {Improving Prediction Accuracy using Random Forest Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Nesma Elsayed and Sherif Abd Elaleem and Mohamed Marie},
  doi       = {10.14569/IJACSA.2024.0150445},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150445}
}