## 4 Case Study
### Discussion  
The highest accuracy obtained by implementing the above classifiers was 74.48% attained by REPTree. REP stands for Reduces Error Pruning. It is a fast decision tree learner that generates multiple trees. Since it is a fast learner, its running time is short. It follows the principle of computing the information gain with entropy. Also, this principle is applied to minimize the error results between the actual and the desired output, which contributes in reaching a high accuracy [59]. Another high-performance model that belongs to the tree category is MSP. It stands for Multivariate Regression prediction model. It has been used in the prediction of student performance and revealed an accuracy of 97.17%. Also, when it is applied in this study, it performed very well with a Root Mean Squared Error reaching a value of 0.43. It quantifies the error at the nodes of the tree and therefore reducing its rate [60]. Moreover, this technique doesn't require any parameter setting or previous knowledge about the domain. The classification steps are fast and simple. All these properties helped in generating a high accuracy. The only highlighted limitations of this technique is that it doesn't perform well with non-numeric data. Also, the classification error rate increases with small training datasets [61]. Followed by the Rule-based classifiers where the results competed closely with the accuracy of REPTree. The Rule based classifiers that were examined are oneR, PART, and ZeroR. All of them performed well in predicting diabetes. They can classify new instances quickly and handle any missing values. Also, they are easy to generate and interpret [62]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multicolumn{5}{c}{**Evaluation Metrics**} \\ \cline{2-6}  & **Accuracy** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline REPTree & 74.48\% & 0.67 & 0.53 & 0.59 & 0.76 \\ M5P & \multicolumn{5}{c}{Root Mean Squared Error = 0.43} \\ KStar & 68.23\% & 0.58 & 0.33 & 0.42 & 0.68 \\ oneR & 70.83\% & 0.61 & 0.46 & 0.52 & 0.65 \\ PART & 74.35\% & 0.70 & 0.47 & 0.56 & 0.77 \\ ZeroR & \multicolumn{5}{c}{Root Mean Squared Error = 0.4771} \\ SMO & 72.14\% & 0.78 & 0.28 & 0.41 & 0.62 \\ BayesNet & 73.83\% & 0.64 & 0.57 & 0.60 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy results of rarely and non-used Machine Learning Classifiers for diabetes prediction. Figure 3: Rarely and non-used Machine Learning Classifiers in diabetes prediction in the last six years. Moreover, one of the classifiers under the function's category has got a significant accuracy of 72.14%. It was attained by the SMO classifier. Sequential Minimal Optimization (SMO) is used in training the Support Vector Machines (SVM). This is due to the need for a solution to the quadratic programming (QP) optimization problem in order to train the SVM. SMO breaks the QP into small and manageable problems that can be solved in less time. Also, the amount of memory required to handle the small problems is minimized. This allows SMO to process very large datasets. Moreover, it has pre-processing abilities in which it replaces the missing values as well as transforming nominal values into binary ones. Furthermore, it performs normalization on the data by default. This helps in boosting the prediction accuracy [63]. For the Bayes category, the Bayes Nets gave remarkable results in many fields such as aircraft systems, scientific researches, and public safety [64]. Although, it performs well, it is not recommended to be used in prediction problems such as this study. This is due to the fact that this algorithm looks for the impact of the variables on the result. It outperforms regression functions when it comes to determine the effect of the variables. The least accuracy was produced by the KStar classifier. It can handle noisy data and it requires less time to train the data. However, its performance becomes better with large datasets.