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- W4283650507 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. In this paper, we propose early prediction of stroke diseases using different data mining-machine learning approaches. The six different classifiers have been trained, namely: Naive Bays (NB) Neural Network(NN), Support Vector Machine(SVM), Random Forest (RF), Decision Tree (DT), k-nearest Neighbor(KNN). Results of the base classifiers have been aggregated using the data mining processes (attribute filters) approach to reach the highest accuracy. Also, here this study has achieved an accuracy about 98.5499 %, where the Hadoop count / Hadoop-Weight performs better than the base classifiers. This model gives the best accuracy for the prediction of stroke. The false-positive rate and false-negative rate of the Hadoop count / Hadoop-Weight are the lowest compared with others." @default.
- W4283650507 created "2022-06-29" @default.
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- W4283650507 date "2022-06-09" @default.
- W4283650507 modified "2023-09-26" @default.
- W4283650507 title "Big Data Processing with Hadoop and Data Mining" @default.
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- W4283650507 doi "https://doi.org/10.1109/hora55278.2022.9800085" @default.
- W4283650507 hasPublicationYear "2022" @default.
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