## 3 Results
Precision, which assesses the proportion of correctly predicted positive observations to the total predicted positives, is \begin{table}
\begin{tabular}{l l|c c c|c c c c} \hline  & **Model** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1** & **Kappa** & **MCC** & **TT \({}^{*}\) (s)** \\ \hline lightgbm & Light Gradient Boosting Machine & 0.7536 & 0.8302 & 0.8001 & 0.7320 & 0.7645 & 0.5072 & 0.5094 & 2.8350 \\ gbc & Gradient Boosting Classifier & 0.7526 & 0.8309 & 0.7946 & 0.7330 & 0.7626 & 0.5053 & 0.5071 & 4.5430 \\ ada & Ada Boost Classifier & 0.7504 & 0.8276 & 0.7736 & 0.7392 & 0.7561 & 0.5008 & 0.5014 & 1.3560 \\ lr & Logistic Regression & 0.7484 & 0.8247 & 0.7675 & 0.7392 & 0.7531 & 0.4968 & 0.4972 & 2.2350 \\ ridge & Ridge Classifier & 0.7469 & 0.0000 & 0.7752 & 0.7337 & 0.7538 & 0.4928 & 0.4938 & 0.1040 \\ lda & Linear Discriminant Analysis & 0.7464 & 0.8220 & 0.7775 & 0.7321 & 0.7541 & 0.4929 & 0.4939 & 0.2320 \\ xgboost & Extreme Gradient Boosting & 0.7456 & 0.8205 & 0.7892 & 0.7259 & 0.7562 & 0.4911 & 0.4930 & 1.0950 \\ qda & Quadratic Discriminant Analysis & 0.7294 & 0.7830 & 0.7823 & 0.7075 & 0.7430 & 0.4589 & 0.4615 & 0.1110 \\ rf & Random Forest Classifier & 0.7254 & 0.7916 & 0.7596 & 0.7110 & 0.7345 & 0.4508 & 0.4519 & 5.7310 \\ nb & Naive Bayes & 0.7221 & 0.7853 & 0.7143 & 0.7257 & 0.7199 & 0.4443 & 0.4443 & 0.0850 \\ svm & SVM-Linear Kernel & 0.7216 & 0.0000 & 0.7648 & 0.7203 & 0.7257 & 0.4433 & 0.4669 & 1.9510 \\ et & Extra Trees Classifier & 0.7093 & 0.7605 & 0.7311 & 0.7005 & 0.7155 & 0.4185 & 0.4190 & 6.4740 \\ knn & K Neighbors Classifier & 0.7090 & 0.7635 & 0.7298 & 0.7008 & 0.7150 & 0.4181 & 0.4185 & 3.0830 \\ dt & Decision Tree Classifier & 0.6559 & 0.6586 & 0.6368 & 0.6621 & 0.6492 & 0.3118 & 0.3120 & 0.2190 \\ dummy & Dummy Classifier & 0.5000 & 0.5000 & 0.2000 & 0.1000 & 0.1333 & 0.0000 & 0.0000 & 0.0690 \\ \hline \end{tabular} \({}^{*}\) TT: Training Time.  
\end{table} 
Table 2: Diabetes prediction: comparison of accuracy, area under the curve (AUC), recall, precision, F1-score, Kappa, and Matthews correlation coefficient (MCC) for different machine learning classifier models. Cells highlighted in yellow emphasize standout performance metrics (i.e., maximum values). \begin{table}
\begin{tabular}{l l|c c c c|c c c} \hline  & **Model** & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1** & **Kappa** & **MCC** & **TT \({}^{*}\) (s)** \\ \hline lightgbm & Light Gradient Boosting Machine & 0.5880 & 0.6788 & 0.5580 & 0.5586 & 0.5399 & 0.1956 & 0.2116 & 19.758 \\ xgboost & Extreme Gradient Boosting & 0.5855 & 0.6739 & 0.5855 & 0.5540 & 0.5416 & 0.1963 & 0.2099 & 8.4470 \\ gbc & Gradient Boosting Classifier & 0.5854 & 0.6748 & 0.5854 & 0.5574 & 0.5330 & 0.1845 & 0.2032 & 51.488 \\ ada & Ada Boost Classifier & 0.5808 & 0.6608 & 0.5808 & 0.5452 & 0.5260 & 0.1726 & 0.1920 & 7.5600 \\ lr & Logistic Regression & 0.5711 & 0.6426 & 0.5711 & 0.5316 & 0.4998 & 0.1333 & 0.1614 & 34.883 \\ rf & Random Forest Classifier & 0.5696 & 0.6463 & 0.5696 & 0.5304 & 0.5253 & 0.1661 & 0.1769 & 20.993 \\ ridge & Ridge Classifier & 0.5682 & 0.0000 & 0.5682 & 0.4934 & 0.4854 & 0.1153 & 0.1497 & 3.5300 \\ lda & Linear Discriminant Analysis & 0.5680 & 0.6387 & 0.5680 & 0.5316 & 0.4933 & 0.1244 & 0.1575 & 5.1920 \\ et & Extra Trees Classifier & 0.5523 & 0.6234 & 0.5523 & 0.5104 & 0.5131 & 0.1395 & 0.1473 & 25.532 \\ dummy & Dummy Classifier & 0.5391 & 0.5000 & 0.5391 & 0.2907 & 0.3777 & 0.0000 & 0.0000 & 3.4860 \\ svm & SVM-Linear Kernel & 0.5144 & 0.0000 & 0.5144 & 0.4911 & 0.4290 & 0.0786 & 0.1121 & 24.822 \\ knn & K Neighbors Classifier & 0.4983 & 0.5761 & 0.4983 & 0.4793 & 0.4873 & 0.0972 & 0.0967 & 12.369 \\ dt & Decision Tree Classifier & 0.4652 & 0.5423 & 0.4652 & 0.4703 & 0.4676 & 0.0777 & 0.0777 & 4.5380 \\ qda & Quadratic Discriminant Analysis & 0.1391 & 0.5109 & 0.1391 & 0.4448 & 0.0719 & 0.0016 & 0.0046 & 4.1150 \\ nb & Naive Bayes & 0.1259 & 0.5740 & 0.1259 & 0.4944 & 0.0561 & 0.0035 & 0.0159 & 3.6190 \\ \hline \end{tabular} \({}^{*}\) TT: training time.  
\end{table}