## 7 Conclusions  
The prevalence of diabetes is not only a burden for the governments in terms of the associated expenditures, but it is also a lifelong strain on diabetic patients. HbA1c and FPG are two important features for diabetes classification. With a dataset having both these important features for diabetes analysis, we constructed two separate datasets that classified an instance into diabetic or non-diabetic class. We found that HbA1c used as a future resulted in better performance (accuracy, precision, and recall) as compared to FPG. Moreover, we also identified several other features like hypertension, weight, and physical activity levels that had an indirect role in diabetic prediction. The LDL/HDL tests were also found to be correlated with diabetic conditions.  
With data from other countries, our approach could be generalized, which may have important implications in the healthcare community. The prescreening of diabetes could be rapid, people could be more aware and educated about their lifestyles, and government expenditures could be reduced alongside the decrease in the significant burden on hospitals due to the prevalence of diabetes. With the ability to predict the onset of diabetes, necessary steps can be taken to avoid the diabetic stage of millions of people who are undiagnosed due to limited resources and lack of awareness. This can not only improve a person's quality of life but also result in a positive impact on the healthcare system. Several recommendations have been proposed in this article in this regard.  
Conceptualization, H.F.A., H.A. and H.M.; methodology, H.M. and H.A.; validation, H.M. and M.S.; formal analysis, H.M., H.F.A., and M.S.; writing--original draft preparation, H.M., H.A., A.A.; writing--review and editing, H.M., H.A., H.F.A., M.S. and A.A. All authors have read and agreed to the submitted version of the manuscript.  
This research was funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia through the project number IFT20129.  
Ethical review and approval were not required for this study, as it did not involve actual humans directly or indirectly in the study. The electronic health records obtained were provided anonymized by the source institutions and the study did not modify or applied any changes to the data. The research only anlayzed a set of data without any referral to any human.  
Anonymized data without any reference to any patient was obtained and used in this research. The data cannot be traced back to the patients, so informed consent was not needed in this research.  
The authors choose not to make the data available yet. It might be available in the future.  
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through project number IFT20129. The authors also acknowledge the Deanship of Scientific Research at King Faisal University for the financial support Institutional Financing Track 2020.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EMR & Electronic Medical Record \\ SVM & Support Vector Machines \\ LR & Logistic Regression \\ DT & Decision Tree \\ RF & Random Forest \\ EVM & Ensemble Voting Model \\ DM & Diabetes Mellitus \\ T1D & Type 1 Diabetes \\ T2D & Type 2 Diabetes \\ HTN & Hypertension \\ PAL & Physical Activity Level \\ \end{tabular}