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RISK PREDICTION OF BREAST CANCER FROM REAL TIME STREAMING HEALTH DATA USING MACHINE LEARNING

Authors: Rasool, Mir Junaid; Brar, Amanpreet Singh; Kang, Hardeep Singh;

RISK PREDICTION OF BREAST CANCER FROM REAL TIME STREAMING HEALTH DATA USING MACHINE LEARNING

Abstract

Background: Mir Junaid Rasool a Computer Science Engineer and Research Student field of interest being Machine learning, Big data science, AI, Web Technologies etc. This article is related to predictive analysis of breast cancer while streams of data is fed continuously in order to perform prediction in real time. The dataset used in this study is “Wisconsin Breast Cancer Data-Set (WBCD)” imported from UCI repository and it needs some preprocessing in order to prepare it for machine learning. In order to carry out this challenge a big data analysis platform which uses in-memory clustering known as Apache Spark can be effectively utilized to monitor data events against machine learning.

{"references": ["L. R. Nair, S. D. Shetty, and S. D. Shetty, \"Applying spark based machine learning model on streaming big data for health status prediction,\" Comput. Electr. Eng., vol. 65, pp. 393\u2013399, 2018, doi: 10.1016/j.compeleceng.2017.03.009.", "A. Ed-Daoudy and K. Maalmi, \"Application of Machine Learning Model on Streaming Health Data Event in Real-Time to Predict Health Status Using Spark,\" Int. Symp. Adv. Electr. Commun. Technol. ISAECT 2018 - Proc., pp. 1\u20134, 2019, doi: 10.1109/ISAECT.2018.8618860."]}

Keywords

Spark, MLlib, Breast Cancer, Big data streaming, Dataframes, Netcat Server

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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