# Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study
## 4 Discussion  
When dealing with diseases such as diabetes it is essential to provide an early and accurate diagnosis. A delayed diagnosis of diabetes can lead to severe health consequences if vigilance is not applied. Therefore, when applying data mining techniques to predict someone's condition, they must be as highly accurate as possible. The cost of a false \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & **AUC** & **CA** & **F1-Score** & **Precision** & **Sensitivity** & **Specificity** \\ \hline Naive Bayes & 0.946 & 0.869 & 0.890 & 0.926 & 0.856 & 0.890 \\ Neural Network & 0.983 & 0.981 & 0.984 & 0.984 & 0.975 \\ AdaBoost & 0.973 & 0.973 & 0.978 & 0.981 & 0.975 & 0.970 \\ kNN & 0.988 & 0.973 & 0.978 & 0.997 & 0.959 & 0.995 \\ Random Forest & 0.997 & 0.969 & 0.975 & 0.981 & 0.969 & 0.970 \\ SVM & 0.993 & 0.971 & 0.977 & 0.975 & 0.978 & 0.960 \\ \hline \hline \end{tabular}
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Table 10: Results of Evaluation Measures for Area Under the Curve (AUC), classification accuracy (CA), F1-Score, Precision, Sensitivity, and Specificity. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multirow{2}{*}{**\(\Sigma\)**} & **Negative** & **Positive** & \(\Sigma\) \\ \cline{2-4}  & **Negative** & 192 & 8 & 200 \\ \cline{2-4}  & **Positive** & 7 & 313 & 320 \\ \cline{2-4}  & **\(\Sigma\)** & 199 & 321 & 520 \\ \hline \hline \end{tabular}
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Table 7: Confusion Matrix of Random Forest Classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**AUC**} & **CA** & **F1-Score** & **Precision** & **Sensitivity** & **Specificity** \\ \hline Naive Bayes & 0.946 & 0.869 & 0.890 & 0.926 & 0.856 & 0.890 \\ Neural Network & 0.983 & 0.981 & 0.984 & 0.984 & 0.984 & 0.975 \\ AdaBoost & 0.973 & 0.973 & 0.978 & 0.981 & 0.975 & 0.970 \\ kNN & 0.988 & 0.973 & 0.978 & 0.997 & 0.959 & 0.995 \\ Random Forest & 0.997 & 0.969 & 0.975 & 0.981 & 0.969 & 0.970 \\ SVM & 0.993 & 0.971 & 0.977 & 0.975 & 0.978 & 0.960 \\ \hline \hline \end{tabular}
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Table 10: Results of Evaluation Measures for Area Under the Curve (AUC), classification accuracy (CA), F1-Score, Precision, Sensitivity, and Specificity. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Prediction**} & \multirow{2}{*}{**\(\Sigma\)**} & **Negative** & **Positive** & \(\Sigma\) \\ \cline{2-4}  & **Negative** & 192 & 8 & 200 \\ \cline{2-4}  & **Positive** & 7 & 313 & 320 \\ \cline{2-4}  & **\(\Sigma\)** & 199 & 321 & 520 \\ \hline \hline \end{tabular}
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Table 8: Confusion Matrix of Support Vector Machine (SVM) Classification. negative is enormously higher than a false positive. If wrongly diagnosed, the subject might take a relaxed posture without knowing their real condition, and this may lead to severe health concerns. Several similar works are available in the literature. In this section, the results of this study are compared with the state of art. In 2019, [PERSON] et al. analyzed a diabetes data set, implementing a Naive Bayes Algorithm, Logistic Regression Algorithm, and Random Forest Algorithm with 10-fold Cross-Validation. Their results exposed the highest accuracy of 97.40% using Random Forest classifier [9]. In 2020, [PERSON] and [PERSON] presented a study comparing data mining classification techniques for diabetes using WEKA Tool. In total, seven algorithms such as Bayes Network, Naive Bayes, Decision tree (J48), Random tree, Random forest, kNN and SVM have been used. The authors registered a 98.07% accuracy, being kNN the classifier with the best performance using 10-fold Cross-Validation [10]. [PERSON] and [PERSON] applied data mining techniques on the PIMA diabetes data set. The authors compared Deep Learning, ANN, Naive Bayes and Decision Tree. According to results, Deep Learning provided the best performance with 98.07% accuracy [11]. A shuffled sampling with 80/20% ratio into the training and validation set has been used.