## 4 Experiments and Results
### Comparison to Previous Work  
Several researchers proposed valuable approaches in the field of diabetes prediction in different ways. Some of them are based on clinical results of diabetes, and some of them are proposed for any specific area or specific situation. Although, our proposed work is generic and based on the genetic information of humans, which is unique information and cannot be denied at any point.  
We approve our model utilizing precision, recall, accuracy, and F1-score. At the same time, different approaches use different evaluation parameters to validate their results. Some of them are specificity, sensitivity, AROC (area under the receiver operating characteristic curve), and MCC (Matthews correlation coefficient), including accuracy, precision, and recall [1,2,14,19,20].  
Sensitivity [13] is the proportion of positive diabetes cases predicted positively.  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{16}\]  
Specificity [13] specifies correctly the negative diabetes cases that were predicted correctly, whereas AROC is plotted between TP and FP rates for discrete cutoff points of a variable.  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{17}\]  
MCC is a correspondence coefficient between the predicted classification and the actual classification [3].  
\[\text{MCC}=\frac{(\text{TP}\times\text{TN})-(\text{FN}\times\text{FP})}{\sqrt {(\text{TP}+\text{FN})\times(\text{TN}+\text{FP})\times(\text{TP}+\text{FP}) \times(\text{TN}+\text{FN})}} \tag{18}\]  
To compare our methods with previous research work, we evaluate our method by using the same evaluation parameters as previous research works. Table 7 displays our proposed model's outcomes according to the performance of the abovementioned metrics.  
Figure 12: Rates of MAE, MSE, and kappa.  
This shows that random forest performs best in all performance matrices and behaves as a perfect classifier for diabetes prediction. However, most previous methods use two or three evaluation parameters [3], such as those that achieved an accuracy of up to 80.8%, specificity of up to 76.7%, sensitivity of up to 84.9%, and MCC of up to 61.8%. Ref. [13] was evaluated based on accuracy that was 85.9%, precision 83.7%, recall 99.7%, F1-score 99.7%, specificity 55.6%, sensitivity 99%, MAE 1%, MSE 8.5%, kappa 97.7%. Ref. [19] used accuracy that was 73.0%, precision at 72.7%, and recall at 73%. Ref. [20] has an area under the ROC curve (AROC) up to 83.4%. Ref. [21] achieved accuracy up to 75.3%, specificity up to 98%, and sensitivity up to 33.2%. Comparing all these evaluation parameters with our evaluated methods, it is evident that our methods outperform by having an average accuracy of 95%. The comparison of our method with previous methods is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms** & **Sensitivity (\%)** & **Specificity (\%)** & **MCC (\%)** & **AROC (\%)** \\ \hline Naive Bayes & 83.9 & 69.8 & 64.5 & 91.5 \\ SMO & 95.9 & 91.2 & 88.7 & 94.1 \\ Simple Logistic & 95.9 & 91.2 & 88.6 & 97.8 \\ Classification via & 92.9 & 85.4 & 83.5 & 98.1 \\ Regression & 93.1 & 85.7 & 82.0 & 96.0 \\ Decision Table & 92.4 & 84.3 & 82.1 & 92.2 \\ Random Forest & 96.6 & 92.7 & 90.3 & 99.5 \\ JRip & 94.6 & 88.6 & 86.0 & 93.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of the proposed model with different performance metrics.  
Figure 13: Comparison of the proposed model (IVSDDM) with previous research works [3; 13; 19; 20; 21].  
This proposed work provides a novel approach to solving human health problems efficiently.