International Journal Of Imaging

Abstract

DETECTION OF STROKE DISEASE USING ML ALGORITHM

Author: Y. SANDHYA, SAMEENA, T. RAKSHITHA, R. RAVITEJA, C. SESHU VARDHAN

Abstract:

Using two machine learning methods, namely Logistic Regression and Novel Adaboost, the study aims to improve the identification of brain stroke using CT scans. A Brain Stroke Prediction Model Using Adaboost and Logistic Regression. There are 30 pictures of healthy human brains and 30 pictures of injured brains in the collection, which has a size of 64.4 MB. Assuming 500 records are employed for training, the test dataset may be recovered from the actual dataset, and the remaining 20% can be used for testing. enhance the precision to the current study, a comparison was made between logistic regression, which also used a sample size of 10 sets, and a new Adaboost method, both of which employed a sample size of 10 sets. Twenty sets were used for the comparison, with ten repetitions each set. The G power test, with ?=0.05 and ?=0.2 as the parameters, yields an around 80%. Logistic Regression has an accuracy rating of 82.21% while the Novel Adaboost Classifier Algorithm has a valu