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. 2020 May 18:2020:8017496.
doi: 10.1155/2020/8017496. eCollection 2020.

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches

Farrukh Khan  1   2 , Muhammad Adnan Khan  1   3 , Sagheer Abbas  1 , Atifa Athar  4 , Shahan Yamin Siddiqui  1   5 , Abdul Hannan Khan  1   5 , Muhammad Anwaar Saeed  6 , Muhammad Hussain  1   2
Affiliations

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches

Farrukh Khan et al. J Healthc Eng. .

Abstract

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Proposed intelligent breast cancer prediction model for BCP-T1F-SVM expert system.
Figure 2
Figure 2
Proposed BCP-T1F expert system methodology.
Figure 3
Figure 3
(a) Rule surface for PET and MRI tumor size. (b) Rule surface for ultrasound and mammography. (c) Rule surface for CT (nodes) and MRI tumor size. (d) Rule surface for biopsy gold (type) and biopsy gold (severity).
Figure 4
Figure 4
Lookup diagram for proposed BCP-T1F expert system.
Figure 5
Figure 5
Proposed BCP-SVM expert system methodology.
Figure 6
Figure 6
Precision chart of proposed BCP-T1F.
Figure 7
Figure 7
Comparisons with previous methods.
See this image and copyright information in PMC

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