# Health Symptom Checking System for Elderly People Using Fuzzy Analytic Hierarchy Process
## 4 Results and Discussion  
To validate the effectiveness of the proposed system, 17 participants (5 females and 12 males) were enrolled in this experiment starting from 1 September 2017 for three months. Each participant downloaded the developed app on their mobile device. Simple instructions were given to each participant to guarantee that they fully understood every function provided by the app. Data were collected from users' queries about the symptoms whenever they felt sick. Then, each datum was transmitted through wifi or the internet to be saved in the backend database for further analysis. We used various metrics, such as accuracy, sensitivity, and specificity [32], for performance evaluation and comparison of other methods to the proposed model. Accuracy: This metric tested its ability to differentiate the recommended disease was the correct disease as assessed by physicians. To estimate the accuracy of a query, the proportion of true positive (TP) and true negative (TN) was calculated in all evaluated cases. \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{13}\]  
Sensitivity: This metric tested its ability to identify the true diseases. To estimate the accuracy of a query, the proportion of TP with respective to sum of TP and false negative (FN) in queries should be calculated. \[Sensitivity=\frac{TP}{TP+FN} \tag{14}\]  
Specificity: The specificity tested its ability to identify the nondisease cases. To estimate the accuracy of a query, the proportion of TN with respective to sum of TN and false positive (FP) in nondisease cases needed to be calculated. \[Specificity=\frac{TN}{TN+FP} \tag{15}\]  
The first case study was based on the 61 queries about diabetes-related symptoms in the database. Table 10 shows the confusion matrix. Note that in Table 10, after a participant had clicked some symptoms on the app, if the most possible disease recommended was diabetes, then it was termed \"Yes\"; otherwise, it was termed \"No\". These results indicate that the accuracy, sensitivity, and specificity were 83.61%, 83.33%, and 84%, respectively. These results verify the effectiveness of the proposed symptom checker in helping users to identify the suspected diseases. A log regression model was applied to find the relationship between the recommended disease and possible symptoms. We used those 61 queries in Table 10 to illustrate the calculation of coefficients in the regression model. To save space, only three queries are listed in Table 11, where the first row reads: a male, aged 57 years, had normal blood pressure, weight loss, high cholesterol, thirst, and obesity. Based on his query, the symptom checker inferred that he had diabetes, and this was confirmed by the physician. The log regression model was calculated as follows:  
\[\log y=\beta_{0}+\beta_{1}x_{1}+\ldots+\beta_{5}x_{5} \tag{16}\] \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Query vs. Outcome**} & \multicolumn{3}{c}{**Outcome**} \\ \cline{2-4}  & & **Yes** & **No** \\ \hline \multirow{2}{*}{Query} & Yes & 30 & 4 \\  & No & 6 & 21 \\ \hline \end{tabular}
\end{table} 
Table 10: Final weights and overall ranking with respect to the goal. where \(y\) denotes diabetes, \(x_{1}\) to \(x_{5}\) represents blood pressure, weight loss, cholesterol, third, and obesity, respectively. \(\beta_{0}\) is the intercept, and \(\beta_{1}\) to \(\beta_{5}\) are the corresponding coefficients of the symptoms. Based on these 61 data, the intercept and coefficients were found to be 0.765, 0.047, 0.121, 0.058, 0.168, and 0.073, respectively. By ranking the coefficients in descending order, third had the highest relative coefficient, at 0.168, to diabetes, followed by weight loss, at 0.121, and blood pressure had the least relationship, at 0.047. The higher the coefficient is, the more important the symptom is with regard to the disease. It was confirmed that patients with diabetes had the thirsty symptom. The other case study applied the weights shown in Table 9 to determine the most possible disease from the users' query.