## 6 Discussion  
Together with other features in the form of lab tests (LDL and HDL) as well as patient's information (age, gender, height, weight, hypertension, and physical activity level), we used HbA1c and FPG as features in two separate datasets for classifying an instance as diabetic or non-diabetic. In our experiments, we found that all five different classifiers predicted with better performance on the FPG-labeled dataset that included HbA1c as one of the input features. This implies that HbA1c can be used as a superior variable than FPG for diabetes prediction. This is consistent with the previous work as well. In a previous study on Vietnamese patients [59], researchers collected overnight fasting blood \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation with 9 Features**} & \multicolumn{4}{c}{**Evaluation with 8 Features**} \\ \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic Regression & 80.86 & 80.95 & 80.86 & 80.83 & 80.86 \(\leftrightarrow\) & 80.95 \(\leftrightarrow\) & 80.86 \(\leftrightarrow\) & 80.83 \(\leftrightarrow\) \\ SVM & **82.10** & **82.30** & **82.10** & **82.05** & **82.10**\(\leftrightarrow\) & **82.30**\(\leftrightarrow\) & **82.10**\(\leftrightarrow\) & **82.05**\(\leftrightarrow\) \\ Decision Tree & 74.07 & 74.07 & 74.07 & 74.06 & 75.31 \(\uparrow\) & 75.34 \(\uparrow\) & 75.31 \(\uparrow\) & 75.28 \(\uparrow\) \\ Random Forest & 81.48 & 81.91 & 81.48 & 81.38 & 80.86 \(\downarrow\) & 81.61 \(\downarrow\) & 80.86 \(\downarrow\) & 80.70 \(\downarrow\) \\ Ensemble & 77.78 & 78.14 & 77.78 & 77.66 & 78.40 \(\uparrow\) & 78.86 \(\uparrow\) & 78.40 \(\uparrow\) & 78.26 \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of the HbA1c-labeled dataset. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation with 9 Features**} & \multicolumn{4}{c}{**Evaluation with 8 Features**} \\ \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic Regression & 83.33 & 83.31 & 83.33 & 83.04 & 82.72 \(\downarrow\) & 82.62 \(\downarrow\) & 82.72 \(\downarrow\) & 82.45 \(\downarrow\) \\ SVM & 84.57 & 84.74 & 84.57 & 84.22 & 84.57 \(\leftrightarrow\) & 84.74 \(\leftrightarrow\) & 84.57 \(\leftrightarrow\) & 84.22 \(samples from 3523 individuals (of which 2356 were women). Like our case, diabetes was diagnosed with an HbA1c value \(\geq 6.5\%\) or an FPG level \(\geq 7\) mmol/l. It was concluded that HbA1c testing had a higher sensitivity for identifying patients at risk for diabetes vs FPG, and therefore may have a greater impact on the diagnoses, cost, burden, and treatment of patients with diabetes [59]. When compared with the existing approaches, we can identify some distinguishing features of our approach. We used only a limited number of basic features (age, gender, height, weight, presence of hypertension, and physical activity level) and three laboratory tests (HDL, LDL, HbA1c, or FPG) to predict if a person has diabetes or not. In contrast, most of the existing approaches use many attributes. For example, [PERSON] et al. [34] initially used 123 features in diabetes prediction and even after removing the various laboratory tests, they were left with a much higher number of features (the exact number is not known). Finding these many features in real-world data is rarely possible. So, we proposed a mechanism whereby only with a few features could we infer the role played by them in the classification of a person into diabetic or non-diabetic. The strategy for the identification of the contribution of each feature through the feature importance is also significant in the current analysis. Mostly, a correlation analysis is performed directly to identify such hidden patterns from data (e.g., as in [44]).