Autors: Ivanova, D. A.
Title: Big Data Analytics for Early Detection of Breast Cancer based on Machine Learning
Keywords: Big Data Analytics, Early Detection, Breast Cancer, Machine Learning

Abstract: This paper presents the concept and the modern advances in personalized medicine that rely on technology and review the existing tools for early detection of breast cancer. The breast cancer types and distribution worldwide is discussed. It is spent time to explain the importance of identifying the normality and to specify the main classes in breast cancer, benign or malignant. The main purpose of the paper is to propose a conceptual model for early detection of breast cancer based on machine learning for processing and analysis of medical big data and further knowledge discovery for personalized treatment. The proposed conceptual model is realized by using Naive Bayes classifier. The software is written in python programming language and for the experiments the Wisconsin breast cancer database is used. Finally, the experimental results are presented and discussed.



    AIP Conference Proceedings, vol. 1910, issue 60016, 2017, United States, AIP,

    Copyright AIP

    Цитирания (Citation/s):
    1. Intelligent method for adaptive in silico knowledge discovery based on big genomic data analytics - 2018 - в издания, индексирани в Scopus или Web of Science
    2. Scalable framework for adaptive in-silico knowledge discovery and decision-making out of genomic big data - 2018 - в издания, индексирани в Scopus или Web of Science
    3. Machine Learning Techniques for Survival Time Prediction in Breast Cancer (Lecture Notes in Computer Science) - 2018 - в издания, индексирани в Scopus или Web of Science
    4. Kirilov, K., Borovska, P., Conceptual model of integrated approach for in silico knowledge data discovery for breast cancer diagnostics and precision therapy, AIP Conference Proceedings 2172,020003 - 2019 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus