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- W4361805567 abstract "A stroke occurs as a result of intense blood flow, causing confusion in the brain as a result of the brain cells not getting enough oxygen and nutrients, and these cells begin to die. It is very important that the cases of stroke are diagnosed early and very accurate, as it contributes to treating the condition or reducing the risks associated with it if it is predicted early. There are many research trends that are based on the databases that are provided to health centers, and some of them rely on databases for research purposes. Where the research is based on a big healthcare dataset. This paper presents a prototype to classify big healthcare dataset stroke attributes that combine Hadoop and machine learning algorithms. Machine learning can be portrayed as a significant tracker in area s like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. The results showed that the proposed system contributes to predicting the occurrence of stroke with an accuracy of about 98% for both systems that are based on Hadoop (Count-Weight)." @default.
- W4361805567 created "2023-04-05" @default.
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- W4361805567 date "2023-01-01" @default.
- W4361805567 modified "2023-10-11" @default.
- W4361805567 title "Attribute selection for stroke prediction based on Hadoop and machine learning" @default.
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- W4361805567 doi "https://doi.org/10.1063/5.0121349" @default.
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