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Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches
المؤلفون المشاركون
Athar, Atifa
Hussain, Muhammad
Khan, Farrukh
Khan, Muhammad Adnan
Abbas, Sagheer
Siddiqui, Shahan Yamin
Khan, Abd al-Hannan
Saeed, Muhammad Anwaar
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-19
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Khan, Farrukh& Khan, Muhammad Adnan& Abbas, Sagheer& Athar, Atifa& Siddiqui, Shahan Yamin& Khan, Abd al-Hannan…[et al.]. 2020. Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1186393
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Khan, Farrukh…[et al.]. Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches. Journal of Healthcare Engineering No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1186393
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Khan, Farrukh& Khan, Muhammad Adnan& Abbas, Sagheer& Athar, Atifa& Siddiqui, Shahan Yamin& Khan, Abd al-Hannan…[et al.]. Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1186393
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1186393
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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