# 8001-30-7 br Sample calculation br Proposed WONN

Sample calculation:

• Proposed WONN-MLB: With ‘1000’ patient data consid-ered for experimentation and number of data correctly diagnosed as disease being ‘930’, the diagnosing accuracy is calculated as follows:
930
DA = ∗ 100 = 93%

• NSCLC: With ‘1000’ patient data considered for experimen-tation and number of data correctly diagnosed as disease being ‘890’, the diagnosing accuracy is calculated as follows:
890
DA = ∗ 100 = 89%

• BSVM: With ‘1000’ patient data considered for experimen-tation and number of data correctly diagnosed as disease being ‘860’, the diagnosing accuracy is calculated as follows:
860
DA = ∗ 100 = 86%

• NPPC: With ‘1000’ patient data considered for experimen-tation and number of data correctly diagnosed as disease being ‘800’, the diagnosing accuracy is calculated as follows:
800
DA = ∗ 100 = 80%

• MV-CNN: With ‘1000’ patient data considered for exper-imentation and number of data correctly diagnosed as disease being ‘740’, the diagnosing accuracy is calculated as follows:
740
DA = ∗ 100 = 74%

Fig. 3 shows the diagnosing accuracy comparison between proposed approach and existing NSCLC and BSVM, respectively. It is found that 8001-30-7 the diagnosing accuracy of lung cancer is improved using WONN-MLB because of measurement of the weak classifier with low weighted error and new component based on error function through ensemble classification. The results confirm that with an increase in the number of patient data, the diagnos-ing accuracy increases for minimum patient data, then reduces with an increase in the number of patient data. This happens because with an increase in the number of patient data, many irrelevant attributes are also present. Moreover, preprocessing performed in the WONN-MLB method, the certain error is oc-curred, which results in certain amount of irrelevant attributes even after preprocessing. However, the comparison made with the existing methods NSCLC, BSVM, NPPC and MV-CNN shows an improvement is observed by using the WONN-MLB method. This happens because of the application of ensemble classification that not only minimizes the error by updating the weak classifier, but also minimizes the time by boosting the updated results. This in turn improves the diagnosing accuracy using WONN-MLB method by 7%, 11%, 19% and 28% as compared to NSCLC, BSVM, NPPC, and MV-CNN, respectively.

4.2. Scenario 2: Impact of false positive rate

The second important parameter used to measure the early diagnosing of lung cancer is the rate of false positive or error, while to conduct multiple comparisons in a statistical framework, the false positive rate refers to the probability of falsely rejecting the null hypothesis for a specific test. In other words, the false positive rate is measured as the ratio between the number of negative events (i.e., not diagnosed with lung cancer) wrongly categorized as positive (i.e., diagnosed with lung cancer) and the

total number of actual negative events (i.e., not diagnosed with lung cancer). It is formulated as follows:
FPR =
ICDdisease
∗ 100
(15)

s

From Eq. (15), the false positive rate ‘FPR’ refers to the ratio of

number of patient data incorrectly diagnosed as disease ‘ICDdisease’ to the total samples ‘s’ considered for experimentation. It is

measured in terms of percentage (%). The values obtained through Eq. (15) are represented as shown in Fig. 5 for different patient data using the proposed WONN-MLB approach and compared it with the NSCLC and BSVM. The sample calculation for measuring false positive rate using the three methods is given as follows:
Sample calculation

• Proposed WONN-MLB: With ‘1000’ number of patient data considered as samples and ‘90’ number of patient data incorrectly diagnosed with lung cancer disease, the false positive rate is as given as follows:

• NSCLC: With ‘1000’ number of patient data considered as samples and ‘120’ number of patient data incorrectly diagnosed with lung cancer disease, the false positive rate is given as fol-lows:
120
FPR =

• BSVM: With ‘1000’ number of patient data considered as samples and ‘140’ number of patient data incorrectly diagnosed with lung cancer disease, the false positive rate is as given as follows:
140
FPR =

• NPPC: With ‘1000’ number of patient data considered as samples and ‘160’ number of patient data incorrectly diagnosed with lung cancer disease, the false positive rate is as given as follows:
160
FPR =

• MV-CNN: With ‘1000’ number of patient data considered as samples and ‘170’ number of patient data incorrectly diagnosed