Abstract
Now a day’s neural network are being used successfully in an increasing number of application areas. Expert system is used comprehensively in several research domains. Heart disease diagnosis is a complicated process and requires high level of expertise. This paper examines and aiming to study a expert system for diagnosing of heart disease using support vector machine, feed forward Back propagation technique, Radial Basis Function, Generalized Regression Neural Network etc with other Data mining techniques that are used for diagnosis for heart diseases. This paper studies the analysis and examines the different approaches used for the detection of heart disease using different neural network techniques and data mining techniques.
Description
In these study, around 300 patient’s information is collected from Hospital, under supervision of Doctor. Using 250 patients data is trained by using RBF, SVM, GRNN and FFBP. Around 50 samples were tested with these two techniques. If the more data set is used for the training the NN model gives more robust results. The analysis model by using SVM and FFBP of ANN gives less appropriate result for medical prescription for heart disease patient. However, there are several techniques that can improve the speed and performance of the back propagation algorithm, weight initialization, use of momentum and adaptive learning rate. So we can conclude that, the result of testing data by using GRNN, SVM and FFBP is not satisfactory but using Radial Basis function the result is satisfactory but by using data mining techniques the result is not satisfactory. So in future these techniques can be used for detection of different disease on the basis of symptoms.
Links and resources
Tags