# Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study
## 2 Materials and Methods
### Performance Metrics  
Performance metrics tell us how a data mining algorithm is performing on a given data set. Consequently, we can compare the results of different algorithms and decide which one performs better or worse. Therefore, Area Under the Curve (AUC), classification accuracy (CA), Precision, Recall/Sensitivity, Specificity and F1-Score have been used. These performance metrics are selected since they are used by most of the related work [9; 10; 11; 12; 13].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute Name** & **Answer** & **Type** \\ \hline Age & & 16–90 & feature \\ Gender & 1. Male & 2. Female & feature \\ Polyuria & 1. Yes & 2. No & feature \\ Polydipsia & 1. Yes & 2. No & feature \\ Sudden weight loss & 1. Yes & 2. No & feature \\ Weakness & 1. Yes & 2. No & feature \\ Polyphagia & 1. Yes & 2. No & feature \\ Genital thrush & 1. Yes & 2. No & feature \\ Visual blurring & 1. Yes & 2. No & feature \\ Ichting & 1. Yes & 2. No & feature \\ Irritability & 1. Yes & 2. No & feature \\ Delayed healing & 1. Yes & 2. No & feature \\ Partial paresis & 1. Yes & 2. No & feature \\ Muscle stiffness & 1. Yes & 2. No & feature \\ Alopecia & 1. Yes & 2. No & feature \\ Obesity & 1. Yes & 2. No & feature \\ Class & 1. Positive & 2. Negative & target \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset attributes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Feature** & **Information Ratio** \\ \hline
1 & Polyuria & 0.3623 \\
2 & Polydipsia & 0.3619 \\
3 & Gender & 0.1720 \\
4 & Sudden weight loss & 0.1518 \\
5 & Partial paresis & 0.1467 \\
6 & Polyphagia & 0.0884 \\
7 & Irritability & 0.0912 \\
8 & Alopecia & 0.0551 \\
9 & Visual blurring & 0.0470 \\
10 & Weakness & 0.0436 \\
11 & Age & 0.0113 \\
12 & Muscle stiffness & 0.0115 \\
13 & Genital thrush & 0.0118 \\
14 & Obesity & 0.0059 \\
15 & Delayed healing & 0.0016 \\
16 & Ichting & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ranked Attributes.  
One of the essential metrics for the evaluation of any classification model's performance is ROC, which is a probability curve. AUC represents the degree or measure of separability. It tells the capacity of the model to distinguish between classes. The higher the AUC, the better the p model's performance at predicting the true positives and the true negatives [29].  
Accuracy (CA) refers to the correct predictions rate. It is given by the division of total correct predictions by the total number of instances.  
\[CA=\frac{TP+TN}{TP+TN+FP+FN}\]  
Precision is also called positive predictive value and reports which of those who were predicted to be positive are actually positive. It is defined as the number of true positives divided by the number of true positives plus the false positives.  
\[Precision=\frac{TP}{TP+FP}\]  
Sensitivity calculates the true positive rate, and this is how many of the actual positives were correctly labelled. It is defined as the number of true positives divided by the number of true positives plus the number of false negatives.  
\[Sensitivity=\frac{TP}{TP+FN}\]  
Specificity defines the true negative rate. This is the proportion of actual negatives which were correctly predicted. To obtain this metric, we divide the number of false positives by the number of true negatives plus the number of false positives.  
\[Specificity=\frac{TN}{TN+FP}\]  
F1-Score is the harmonic mean of Precision and Recall. It presents a better measure of the incorrectly classified cases than the CA.  
\[F_{1}=2\cdot\frac{precision\cdot sensitivity}{precision+sensitivity}\]