# Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study
## 4 Discussion
In 2020, [PERSON], [PERSON], [PERSON], and [PERSON] analysed a diabetes data set proposing a model based on ANNs. Their study proved the highest accuracy of 88% efficacy [12]. In 2018, [PERSON], [PERSON], and [PERSON] conducted a study using Orange Tool to analyse a diabetes data set. Authors implemented and compared Random Forest, Feed-Forward Artificial Neural Networks, kNN, SVM and Decision Tree. The results revealed ANNs to be the algorithm with the highest accuracy. It correctly predicted 93.85% of cases [13]. Table 11 presents the comparative results between studies. The proposed Neural Network classifier presented better results than any of the other studies mentioned above. [PERSON] and [PERSON] reported higher accuracy of 98.07% using kNN classifier [10] and [PERSON] et al. report 98.62% [14]. Our study showed a result of 98.08% employing Neural Network. The results show an AUC of 98.3%. The ROC curve for the negative and positive class is presented in Scalable Vector Graphics (SVG) format as Supplementary Files 2 and 3, respectively. kNN classifier also presents outstandingly results. It achieved 97.31% of accuracy, however, [PERSON] and [PERSON] [10] and [PERSON] et al. report 98.62% [14] presented better results. With 97.12% of accuracy, the SVM algorithm is the third most accurate classifier. [PERSON] and [PERSON] reported 92.11% [10], and [PERSON], [PERSON], and [PERSON] reported 78.51% [13]. Random Forest also presented reliable results when compared to the other studies. It achieved 96.90% of accuracy, presenting a better result than [PERSON], [PERSON], and [PERSON] reporting 91.34% [13]. However, the authors of [9,10,14] reported 97.40%, 97.50% and 98.8%, respectively. Naive Bayes classifier has presented the lowest accuracy of 86.92%. However, it performed better than the [PERSON] and [PERSON] study, which presented 76.33% efficiency [11]. [PERSON] and [PERSON] study [10] and [PERSON], [PERSON], [PERSON], and [PERSON] [9] outperform our result. Their research reports values of 87.11 and 87.4%, respectively. Finally, the proposed AdaBoost method provides 97.3% accuracy, which is \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **N.B.1** & **ANN** & **kNN** & **R.F.2** & **SVM** & **AdaBoost** \\ \hline
[9] & **87.4\%** & - & - & 97.4\% & - & - \\
[10] & 87.11\% & - & 98.07\% & 97.5\% & 92.11\% & - \\
[11] & 76.33\% & 90.34\% & - & - & - & - \\
[12] & - & 88\% & - & - & - & - \\
[13] & - & 93.85\% & 90.85\% & 91.34\% & 78.51\% & - \\
[14] & 79\% & 80.47\% & **98.62\%** & **98.8**\% & 75.29\% & 77.16\% \\ This study & 86.92\% & **98.08\%** & 97.31\% & 96.9\% & **97.12\%** & **97.3**\% \\ \hline \hline \end{tabular}  
* Naive Bayes; 2 Random Forest. The bold numbers represent highest value.  
\end{table} 
Table 11: Studies Results Comparison. higher than the results proposed by [PERSON] et al. [14] (77.16%). Table 11 includes studies that have used different data sets. This is a limitation regarding the comparison of the results. We use a recent data set, and the number of studies that use the same data source is limited. Therefore, we have compared our results with works that use a different data source following the approach suggested by [14]. Using a 10-fold Cross-Validation technique, we ensure that all the data has been used for training and testing to prevent over-fitting and under-fitting. It is critical to note that some of the cited studies do not present a clear explanation of the parameters used. Consequently, we could not reproduce their results. Nevertheless, all the studies have their own limitations. After a systematic evaluation, we can confirm that the Neural Network is the classification technique that performed most competently. However, while it may fit best for this data set, it might not be the case when applied to another. The data set is also limited. It does not consider family history of diabetes, consumption of certain prescription drugs, smoking, and sleep deprivation.