Predictive Model for Brain Stroke in CT using Deep Neural Network
Maya B S1, Asha T2
1Maya B S , Assistant Professor in department of computer science & Engineering, Bangalore Institute of Technology, Bangalore.
2Dr. Asha. T, Assistant Professor in department of computer science & Engineering, Bangalore Institute of Technology, Bangalore.
Manuscript received on April 30, 2020. | Revised Manuscript received on May 06, 2020. | Manuscript published on May 30, 2020. | PP: 2011-2017 | Volume-9 Issue-1, May 2020. | Retrieval Number: F9954038620/2020©BEIESP | DOI: 10.35940/ijrte.F9954.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Stroke, Deep Neural Network Learning, Gaussian Naïve Bayesian Classifier, Principal Component Analysis, Machine Learning Algorithm.
Scope of the Article: Machine Learning