Artificial Intelligence Algorithms and their Role in Assessing the Financial Health of Municipalities in Algeria based on the Logistic Regression Model,

Section: Research Paper
Issue
Vol. 21 No. 2 (2024): Volume 21 Issue 2
Published
Jun 25, 2025
Pages
109-121

Abstract

Using a binomial logistic regression model, through this paper we attempt to study the relationship between the state of financial health of municipalities in Algeria, based on the wealth index, in relation to a group of independent variables related to their revenues, the size of spending, and some variables that reflect aspects of this spending and their various specializations. Logistic regression is one of the classification models, and it is considered an alternative model to linear regression models, because this type of model has the property of predicting the probability of the occurrence or non-occurrence of the values of the nominal dependent variables based on a set of explanatory variables (independent variables), whether in their quantitative or qualitative type.

References

  1. ALbazzaz, Z.M. and O.B. Shukur. Using LSTM Network Based on Logistic Regression Model for Classifying Solar Radiation Time Series. in International Conference on Explainable Artificial Intelligence in the Digital Sustainability. 2024. Springer.
  2. Ani, C., U. Hassan, and M. Maiwando, Prediction of Banks Stock Performance in Nigeria Stock Exchange Market: An Application of Logistic Regression Model. Abacus (Mathematics Science Series), 2020. 44(1).
  3. Baima, G., et al., Periodontitis prevalence and severity in inflammatory bowel disease: A casecontrol study. Journal of periodontology, 2023. 94(3): p. 313-322.
  4. Costa e Silva, E., et al., A logistic regression model for consumer default risk. Journal of Applied Statistics, 2020. 47(13-15): p. 2879-2894.
  5. Fernando, R., Logit, probit and tobit: Models for categorical and limited dependent variables. PLCS/RDC Statistics and Data Series at the West, 2011.
  6. Kassens, A.L., Theory vs. practice: Teaching undergraduate econometrics. The Journal of Economic Education, 2019. 50(4): p. 367-370.
  7. Osisanwo, F., et al., Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 2017. 48(3): p. 128-138.
  8. Park, H.-A., An introduction to logistic regression: from basic concepts to interpretation with particular attention to nursing domain. Journal of Korean academy of nursing, 2013. 43(2): p. 154-164.
  9. Rakotomalala, R., Pratique de la Rgression Logistique. Rgression Logistique Binaire et Polytomique. Version 2.0. Lyon, Universit Lumire Lyon-2, multigr, 2014.
  10. Random Under Sampler, in API reference. https://imbalanced-learn.org/stable/references/generated/imblearn.under_sampling.RandomUnderSampler.html.
  11. Sagarra, M., C. Mar-Molinero, and M. Garca-Cestona, Spanish savings banks in the credit crunch: could distress have been predicted before the crisis? A multivariate statistical analysis. The European Journal of Finance, 2015. 21(3): p. 195-214.
  12. Shukur, O.B. and O.A. Malaa, Comparison of Logistic regression, Convolution Neural Network, and Kernel Approaches for Classifying the Caenorhabditis Elegans Motion. Iraqi Journal of Statistical Sciences, 2023. 20(2): p. 175-187.
  13. Shukur, O.B., Using hybrid procedure in generalized additive model and classification tree to classify agriculture evaporation in Mosul city. Int. J. Agricult. Stat. Sci. Vol, 2021. 17(1): p. 1509-1514.
  14. Swamynathan, M., Mastering machine learning with python in six steps: A practical implementation guide to predictive data analytics using python. 2017: Springer.
  15. Tareq, W.K. and O.B. Shukur, Using Cart Approach for Classifying Climatic Status of Mosul City. Journal Of Agricultural And Statistical Sciences, 2021. 17: p. 2325-2331.
  16. Yuval, M., B. Yaman, and . Tosun, Classification comparison of machine learning algorithms using two independent CAD datasets. Mathematics, 2022. 10(3): p. 311.

Author

Charif, Aicha Salah

Laboratory of globalization and economic policies, University of Algiers 3, Algiers.

ORCID

Identifiers

Download this PDF file
PDF

Statistics

How to Cite

Aicha Salah, C. (2025). Artificial Intelligence Algorithms and their Role in Assessing the Financial Health of Municipalities in Algeria based on the Logistic Regression Model,. IRAQI JOURNAL OF STATISTICAL SCIENCES, 21(2), 109–121. Retrieved from https://rjps.uomosul.edu.iq/index.php/stats/article/view/21009