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Cerebrovascular Attack Detection Using Artificial Intelligent Neural Network

Bagheri, Mahdi | 2018

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 50790 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Bagheri Shouraki, Saeed; Haj Sadeghi, Khosrow
  7. Abstract:
  8. Cerebrovascular Attack has been ranked the second or third of top 10 death causes in Taiwan. It has caused about 13,000 deaths every year since 1986. Once Cerebrovascular Attack (CVA) occurs, it not only leads to the huge cost of medical care, but even death. All developed countries in the world put CVA prevention and treatment in high priority. However, it is necessary to build a detective model to enhance the accurate diagnosis of CVA. From this detective model, CVA classification rules were extracted and used to improve the diagnosis and detection of CVA. This study acquired 2449 valid samples from this CVA prevention and treatment program, and adopted three classification algorithms, multi-layer perceptron neural network (MLP), radial basis function neural network (RBF) and support vector machine (SVM) to construct classification models, respectively. After analyzing and comparing classification efficiencies – sensitivity, specificity and accuracy, the radial basis function constructed model was chosen as the optimum detection model for the CVA. In this model, the sensitivity, specificity and accuracy were 81%, 80% and 81%, respectively. 290 important influence factors of detecting Cerebrovascular Attack were also considered. Three experienced cerebrovascular doctors assessed these rules, and confirmed them to be useful to the current clinical medical condition. The aim of this project is to design an application to make the patients aware whether or not they have CVA symptoms. Providing a questionnaire to be completed by patients for recording the clinical symptoms can help designing such application. It helps to improve application performance for detecting CVA
  9. Keywords:
  10. Accuracy ; Sensitivity ; Support Vector Machine (SVM) ; Multi-Layer Perceptron (MLP) ; Artificial Neural Network ; Radial Basis Function ; Cerebrovascular Attack (CVA)

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