## 5 Results
### Measuring Diagnostic Accuracy  
Measuring the diagnostic accuracy of a new diagnostic application involves comparing its performance with established reference points provided by other applications. By evaluating the diagnostic results produced by the new application and comparing them to the results obtained from established apps, it is possible to assess the application's ability to correctly identify and classify medical conditions. Key metrics such as sensitivity, specificity, positive predictive value, and negative predictive value can be calculated to quantitatively evaluate the performance of the new application [52]. According to [52], these parameters are ideally used to calculate the accuracy of diagnosis and to elucidate their respective meanings. 1. True positive (TP): This refers to the number of individuals correctly identified as positive by the test. In medical terms, it represents the number of people with the condition being tested who were correctly identified as having the condition. 2. True negative (TN): This represents the number of individuals correctly identified as negative by the test. It refers to individuals without the condition who were correctly identified as not having the condition. 3. False positive (FP): These are the individuals who were incorrectly classified as positive by the test but do not have the condition. In medical terms, it represents the number of people who were mistakenly identified as having the condition when they were actually healthy. 4. False negative (FN): These are the individuals who were incorrectly classified as negative by the test, but actually have the condition. It represents the number of individuals who were mistakenly identified as not having the condition when they actually did. However, the representation is modified for each test to compare the diagnosis of the app with already proven or existing parameters. Test 1 is a comparison of the diagnosis provided by the designed app and a doctor and Test 2 is a comparison of the diagnosis provided by the designed application and an existing AI-based diagnostic Android application (see the confusion matrices in Table 4 and Table 5 respectively). The performance metrics for both tests are provided in Table 6.  
**True positive (TP):** Number of CVD cases correctly identified by the app and that matches the preexisting diagnosis of the doctor (test 1)/AI-diagnosis android app (test 2). \begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & **Prototype Application** & **Existing Application** \\ \hline
1 & Hypertension, heart failure & hypertension, heart failure, heart disease \\ \hline
2 & CHD, heart attack, heart failure & COVID-19, heart disease \\ \hline
3 & COVID-19, heart failure & COVID-19, ischemic heart disease, pneumonia \\ \hline
4 & Early stroke, hypertension, CBD & Stroke, pancreatic neoplasms, hypertension \\ \hline
5 & Cirrhosis & Aortic dissection, anaphylaxis, pulmonary hypertension \\ \hline
6 & Cirrhosis & COVID-19, iron deficiency \\ \hline
7 & Heart attack, hypertension, heart failure, early stroke & Heart failure, type disease, COVID-19, stoke \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of diagnoses suggested by the prototype application and an existing application. **True negative (TN):** Number of CVD cases identified as negative (that is, not identified as CVD) by the app and by the doctor (Test 1)/AI-diagnosis Android app (Test 2). **False positive (FP):** Number of cases of CVD that were inaccurately identified by the app, despite the absence of the actual condition, as proven by a doctor or AI-based diagnosis Android app. **False negative (FN):** Number of cases of CVD that went undetected by the app, but were actually identified and diagnosed by a doctor or an AI-based diagnosis Android app. **Actual positive:** Possible CVD diagnosis that was evaluated by a doctor or an existing application, with supporting evidence or validation.