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Accepted 25 October 2019

Comparison of Statistical Logistic Regression and RandomForest Machine Learning Techniques in Predicting Diabetes

Tahani Daghistani and Riyad Alshammari

Abstract: Diabetes is one of the global concerns in the healthcare domain and one of the leading challenges locally in Saudi Arabia. The prevalence of diabetes is anticipated to rise; early prediction of individuals at high risk of diabetes is a significant challenge. This study aims to compare RandomForest machine learning algorithm and Logistic Regression algorithm towards the prediction of diabetes. We analyzed 66,325 records that extracted from the Ministry of National Guard Hospital Affairs (MNGHA) databases in Saudi Arabia between 2013 and 2015. Both Machine Learning algorithms were applied to predict diabetes based on 18 risk factors. The evaluation criteria to compare the two algorithms were based on precision, Recall, True Positive rate, False Negative rate, F-measure and Area under the curve. The overall prevalence of diabetes in the data set is 64.47%. Male represents 55.50% of the data set while female represents 44.50%. For RandomForest (RF) model, the precision, Recall, True Positive Rate, False Positive Rate and F-measure value for predicting diabetes were 0.883, 0.88, 0.88, 0.188 and 0.876, respectively, while Logistic Regression model were only 0.692, 0.703, 0.703,0.454 and 0.675, respectively. Area under the ROC curve (AUC) value was 0.944 for the RF model and 0.708 for Logistic Regression model , which demonstrates higher predictive performance for RF than the Logistic Regression model. The RF algorithm showed superior prediction performance over Logistic Regression technique in predicting diabetes based on various matrices.


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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