| Autor: |
Vishnu Kirthiga R, Ponmalar A, Pavithra P, Nokudaiyaval G, Sri Rakshya R.V.T |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 6th International Conference on Communication and Electronics Systems (ICCES). |
| DOI: |
10.1109/icces51350.2021.9489055 |
| Popis: |
Stroke causes the unpredictable death and damage to multiple body components. If a stroke is identified early enough, it is possible to receive the appropriate therapy and recover from the stroke. As a result, this research work attempts to develop a stroke prediction system to assist doctors and clinical workers in predicting strokes in a timely and efficient manner. The proposed machine learning algorithm helps in predicting whether or not a person is suffering from a stroke. In order to determine the presearcnce or absence of stroke, this research work has considered m ore than twelve criteria. The proposed machine learning algorithm has expected to achieve a higher level of accuracy of 99%, therefore this research work has employed an Artificial Neural Network to achieve the highest efficiency and accuracy. Whereas, the existing system has used Random forest and XGBoost algorithm to predict the stroke this research work has attempted to use Artificial Neural Network [ANN] to get the expected output. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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