## 5 Conclusions  
This paper proposes a method to discriminate between patients affected by diabetes and those not affected by using machine-learning algorithms and artificial neural networks. We evaluated our approach using real-world data extracted from the Omani well-being program. We then compared it with a widely used dataset by researchers, the Pima Indian Diabetes dataset, which describes a population near Phoenix, Arizona. Training the model using seven different classification algorithms, we obtained the equal highest accuracy with random forest and decision tree with an accuracy of 98.37%. The proposed model shows significant performance in selecting the most accurate predictors of diabetes. In the future, it would be intriguing to see the classification of additional types of medical data under this framework, thereby providing a cost-effective and time-saving option for  
Figure 18: Comparative performance of our proposed method in both datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **PID Prediction Speed** & **PID Training Time** &
\begin{tabular}{c} **Oman Prediction** \\ **Speed** \\ \end{tabular} & **Oman Training Time** \\ \hline K-nearest neighbours & \textasci{-}24,000 obs/s * & 0.53 s & \textasci{-}15,000 obs/s & 0.61 s \\ Support vector & \textasci{-}18,000 obs/s & 54.72 s & \textasci{-}19,000 obs/s & 0.54 s \\ machine; & \textasci{-}26,000 obs/s & 0.65 s & \textasci{-}15,000 obs/s & 0.93 s \\ Decision tree & \textasci{-}58,000 obs/s & 0.44 s & \textasci{-}22,000 obs/s & 1.07 s \\ Random forest & \textasci{-}7000 obs/s & 1.44 s & \textasci{-}6500 obs/s & 1.67 s \\ Linear discriminant & \textasci{-}35,000 obs/s & 0.78 s & \textasci{-}17,000 obs/s & 0.93 s \\ analysis & \textasci{-}12,000 obs/s & 1.93 s & \textasci{-}12,000 obs/s & 1.93 s \\ \hline \hline \end{tabular}
* obs/s: Number of observations processed per second.  
\end{table}
Table 8: Comparison of time complexity and models training speed.  
diabetic patients and physicians. More attributes can boost performance and provide more accurate forecasts. Moreover, feature selection techniques can be explored.  
Originality of dataset, K.A.S.; Conceptualisation, K.A.S. and W.B.; software, K.A.S.; validation, K.A.S.; data curation, K.A.S.; visualisation, K.A.S.; supervision, W.B.; writing, K.A.S. and W.B.; writing--original draft preparation, K.A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was approved by the Research and Ethical Review & Approval Committee, Ministry of Health, Oman (Proposal ID: MoH/CSW20/24055, 23/122020). This study does not involve humans or animals.  
Not applicable.This study did not involve humans.  
The Espcialy created Omani Prediabetes dataset that support the findings of this study can be available from the corresponding author upon reasonable request. Pima Indian dataset can be found here: [[https://www.kaggle.com/kumargh/pimaindiansdiabetescsv](https://www.kaggle.com/kumargh/pimaindiansdiabetescsv)]([https://www.kaggle.com/kumargh/pimaindiansdiabetescsv](https://www.kaggle.com/kumargh/pimaindiansdiabetescsv)) (accessed on 15 November 2021).  
The authors declare no conflict of interest.