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New Approach for Risk Estimation Algorithms of BRCA12 Negativeness Detection with Modelling Supervised Machine Learning Techniques
المؤلفون المشاركون
Yazici, Hulya
Odemis, Demet Akdeniz
Aksu, Dogukan
Erdogan, Ozge Sukruoglu
Tuncer, Seref Bugra
Avsar, Mukaddes
Kilic, Seda
Turkcan, Gozde Kuru
Celik, Betul
Aydin, Muhammed Ali
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-7، 7ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-09
دولة النشر
مصر
عدد الصفحات
7
التخصصات الرئيسية
الملخص EN
BRCA1/2 gene testing is a difficult, expensive, and time-consuming test which requires excessive work load.
The identification of the BRCA1/2 gene mutations is significantly important in the selection of treatment and the risk of secondary cancer.
We aimed to develop an algorithm considering all the clinical, demographic, and genetic features of patients for identifying the BRCA1/2 negativity in the present study.
An experimental dataset was created with the collection of the all clinical, demographic, and genetic features of breast cancer patients for 20 years.
This dataset consisted of 125 features of 2070 high-risk breast cancer patients.
All data were numeralized and normalized for detection of the BRCA1/2 negativity in the machine learning algorithm.
The performance of the algorithm was identified by studying the machine learning model with the test data.
k nearest neighbours (KNN) and decision tree (DT) accuracy rates of 9 features involving Dataset 2 were found to be the most effective.
The removal of the unnecessary data in the dataset by reducing the number of features was shown to increase the accuracy rate of algorithm compared with the DT.
BRCA1/2 negativity was identified without performing the BRCA1/2 gene test with 92.88% accuracy within minutes in high-risk breast cancer patients with this algorithm, and the test associated result waiting stress, time, and money loss were prevented.
That algorithm is suggested be useful in fast performing of the treatment plans of patients and accurately in addition to speeding up the clinical practice.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Yazici, Hulya& Odemis, Demet Akdeniz& Aksu, Dogukan& Erdogan, Ozge Sukruoglu& Tuncer, Seref Bugra& Avsar, Mukaddes…[et al.]. 2020. New Approach for Risk Estimation Algorithms of BRCA12 Negativeness Detection with Modelling Supervised Machine Learning Techniques. Disease Markers،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1154045
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Yazici, Hulya…[et al.]. New Approach for Risk Estimation Algorithms of BRCA12 Negativeness Detection with Modelling Supervised Machine Learning Techniques. Disease Markers No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1154045
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Yazici, Hulya& Odemis, Demet Akdeniz& Aksu, Dogukan& Erdogan, Ozge Sukruoglu& Tuncer, Seref Bugra& Avsar, Mukaddes…[et al.]. New Approach for Risk Estimation Algorithms of BRCA12 Negativeness Detection with Modelling Supervised Machine Learning Techniques. Disease Markers. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1154045
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1154045
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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