Open AccessPredictive Model for Brain Stroke in CT using Deep Neural Network
Author(s) -
M. S.,
T. Asha
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f9954.059120
Subject(s) - artificial intelligence , random forest , artificial neural network , decision tree , machine learning , stroke (engine) , computer science , naive bayes classifier , principal component analysis , classifier (uml) , population , bayesian probability , statistics , medicine , support vector machine , mathematics , engineering , mechanical engineering , environmental health
The increasing in the incidence of stroke with aging world population would quickly place an economic burden on society. In proposed method we use different machine learning classification algorithms like Decision Tree, Deep Neural Network Learning, Maximum Expectization , Random Forest and Gaussian Naïve Bayesian Classifier is used with associated number of attributes to estimate the occurrence of stroke disease. The present research, mainly PCA (Principal Component Analysis) algorithm is used to limit the performance and scaling used to be adopted to extract splendid context statistics from medical records. We used those reduced features to determine whether or not the patient has a stroke disorder. We compared proposed method Deep neural network learning classifier with other machine-learning methods with respect to accuracy, sensitivity and specificity that yields 86.42%, 74.89 and 88.49% respectively. Hence it can be with the aid of both patients and medical doctors to treat viable stroke.