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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 51825 (01)
- University: Sharif University of Technology
- Department: Industrial Engineering
- Advisor(s): Akhavan Niaki, Taghi
- Abstract:
- Poor decision making in health care has always had irreparable consequences for society. Also, expensive medical tests cause lots of problems for patients. A huge amount of data is produced daily by hospitals, which unfortunately are not used to improve decision making and predicting disease. Data mining can be an appropriate tool for extracting knowledge from a huge amount of data by using a variety of techniques such as prediction. The leading cause of death in the world is heart disease so this study has been designed to predict the incidence of that. Regarding the literature review, the Naïve Bayes method had predicted heart disease accurately. According to the specialist's opinion, some of the features in the heart dataset are highly correlated, which is in contrast with the independence assumption in the Naïve Bayes method. Many methods have been proposed that can improve naïve Bayes by considering dependencies in constructing to relax independence assumption. But a few methods have used pre-processing procedures for naïve Bayes classifier to improve the accuracy of prediction. So in this research, a new pre-processing procedure has been proposed that is able to improve the Naïve Bayes performance. This new procedure sorts attributes by using a new algorithm based on their dependencies then select and change a subset of attributes using PCA or ICA methods. The advantages of this method are short training time, low computational cost and good accuracy
- Keywords:
- Health Care System ; Forecasting ; Data Mining ; Bayesian Analysis ; Heart Diseases
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