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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM21647

Early Detection of Brain Stroke using Machine Learning Techniques

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM21647

Volume:

7 |

5 |

May 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

BRS

Bhoomika R S

AJA

Aishwarya J Annigeri

HD

Himaja Desiraju

HKG

Harsh Kumar Goswami

PVK

Prof. Vani K.A

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Abstract

As in line with the WHO, stroke is the principle source of death and incapability round the world . It is a clinical condition that causes brain damage by way of tearing blood vessels.It is able to likewise take place whilst the development of blood and unique dietary supplements to the cerebrum is disturbed. Most of exploration has targeted on foreseeing breathing screw ups, however not very many examinations have focused on the gamble of a mind assault. Numerous device learning fashions are being advanced with this in mind and are expecting the possibility of a mind stroke. Various physiological elements were used on this paper to educate various fashions for correct prediction using 10 machine studying algorithms inclusive of logistic regression, decision tree type, k-nearest acquaintances, random forest classifier, svm ,adaboost classifier,bernoullinb,mlp,bagging classifier and lgbm.

KEYWORDS

stroke logisticregression decisiontree knn svm ran domforest adaboost bernoullinb mlp bagging lgbm flask

Cite This Article

Bhoomika R S, Aishwarya J Annigeri, Himaja Desiraju, Harsh Kumar Goswami, Prof. Vani K.A, "Early Detection of Brain Stroke using Machine Learning Techniques," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 5, May 2023. https://doi.org/10.55041/IJSREM21647

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Publication Date

5/23/2023

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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