Designing of Clinical Decision Support System for Heart Disease Diagnosis Using Data Mining Techniques

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Sali, Rasoul | 2012

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 43682 (01)
University: Sharif University of Technology
Department: Industrial Engineering
Advisor(s): Shavandi, Hassan
Abstract:
In this study hybrid classification models by combining genetic algorithm and classifiers such as neural network and decision tree are presented and efficiency of these models is tested on 5 different databases against other proposed models in this area. This comparison shows that the model obtained by combining genetic algorithm and neural network obtains better results than other models. Afterwards this model is used as a decision support system in diagnosis heart disease and in addition to determining the parameters of neural network such as the number of hidden layers and the number of neurons in each layer, efficient features in diagnosis heart disease are also determined. Among the considered features, blood pressure, dyslipidemia, age, family background, education, diabetic state, food regime and smoking have been determined as efficient features.
Keywords:
Data Mining ; Classification ; Hybrid Genetic Algorithm ; Neural Network ; Decision Making Support System

No TOC