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  • br with lung cancer disease the false positive

    2020-08-18

    
    with lung cancer disease, the false positive rate is as given as follows: 170 FPR =
    From Eq. (15), the false positive rate for different number of patient data in the range of 1000 to 10,000 is measured. The results of experimental evaluations conducted to measure the false positive rate as shown in Table 1. The false positive rate obtained using the proposed WONN-NLB approach offers comparable values than the state-of-the-art methods.
    Fig. 4 shows the performance analysis of false positive rate for disease diagnosis for big data. As illustrated in Fig. 4, when 1000 number of patient data is considered as samples, 90 patient data were incorrectly diagnosed with lung cancer using WONN-MLB, 120 patient data were incorrectly diagnosed with lung cancer using NSCLC, 140 patient data are incorrectly diagnosed with lung cancer using BSVM, 160 patient data were incorrectly diag-nosed using NPPC, 170 patient data were incorrectly diagnosed using MV-CNN. The false positive rate using WONN-MLN is mini-mized by 25%, 36%, 44% and 47% as compared to NSCLS, BSVM, NPPC, and MV-CNN, respectively. This result is achieved with Newton–Raphsons MLMR preprocessing model. The advantage of applying MLMR preprocessing model is that FICZ instead of using all the attributes in the dataset, only the maximum likelihood and relevancy attributes are considered for disease diagnosis. With the application of log-likelihood function, the attribute availabil-ity also gets changed and reflected in the maximum relevance minimum redundancy coefficient. This adaptive change made through maximum relevance minimum redundancy coefficient in terms minimizes the incorrect lung cancer diagnosis using the WONN-MLN method. The resultant attributes are then used to classify the patients as lung cancer and normal patient which in turn minimizes the false positive rate by 39%, 53%, 58% and 61% as compared to NSCLS, BSVM, NPPC, and MV-CNN, respectively.
    4.3. Scenario 3: Classification time
    The third parameter considered for the early diagnosis of lung cancer is the classification time. The classification time refers to the time taken to classify the patient data as diagnosed with lung cancer or not diagnosed with lung cancer. The classification time is calculated as follows:
    Fig. 4. Performance measure of false positive rate.
    Fig. 5. Performance measure of classification time.
    From Eq. (16), the classification time ‘CT ’ is calculated ac-cording to the samples ‘s’ and the time consumed to perform ensemble classification ‘Time (f (WS))’. Lower the classification time, early the lung cancer diagnosis is said to be. It is mea-sured in terms of milliseconds (ms). The values obtained through Eq. (16) are represented in Fig. 4 with the proposed WONN-MLB approach, existing NSCLC and BSVM. The sample calculation for classification time using the three methods is given as follows: Sample calculations:
    • Proposed WONN-MLB: With the time taken for classifica-tion of single patient data being ‘0.0085 ms’, with ‘1000’ number of patient data considered as samples, the classification time is calculated as follows:
    • NSCLC: With the time taken for classification of single pa-tient data being ‘0.0089 ms’, with ‘1000’ number of patient data considered as samples, the classification time is given as follows:
    • BSVM: With the time taken for classification of single pa-tient data being ‘0.0093 ms’, with ‘1000’ number of patient data considered as samples, the classification time is given as follows:
    • NPPC: With the time taken for classification of single pa-tient data being ‘0.0115 ms’, with ‘1000’ number of patient data considered as samples, the classification time is given as follows:
    • MV-CNN: With the time taken for classification of single patient data being ‘0.0132 ms’, with ‘1000’ number of patient data considered as samples, the classification time is given as follows:
    Fig. 5 shows the measure of classification time to classify the patient data with diagnosed as disease or not, the proposed approach is implemented in Java Language using various numbers of patient data in the range of 1000 to 10,000. The experimental
    Fig. 6. Performance measure of F1-score.
    result of classification time using proposed method is compared with existing NSCLC and BSVM. When considering 1000 num-ber of patient data for the experimental work, the proposed method consumed 8.5 ms to classify, whereas the existing NSCLC, BSVM, NPPC and MV-CNN FICZ consumed 8.9 ms, 9.3 ms, 11.5 ms, and 13.2 ms, respectively. Thus, DNA hybridization is clear that the classification time using proposed approach is less as compared to other ex-isting methods [1,2]. However, with an increase in the number of patient data and increase in the number and size of the at-tributes, the classification time is also increases using all the three methods. Comparative analysis shows that the classification time using proposed approach is less than the [1–3] and [17] methods. This is because of the application of the Newton–Raphson’s Max-imum Likelihood model in addition to the maximum relevance minimum redundancy factor, which applies the first derivate and the second derivate to extract the most relevant attributes. With this most relevant attributes extracted, the classification time is reduced using proposed approach by 34%, 51%, 56%, and 59% as compared to NSCLC by Wu et al.. [1], BSVM by Zięba et al. [2], NPPC by Ghorai et al. [3], and MV-CNN by Liu et al. [17], respectively.