Cardiovascular disease prediction system using genetic algorithm and neural network

2012 ◽  
Author(s):  
N. G. Bhuvaneswari Amma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cardiovascular Disease ◽  
Disease Prediction ◽  
Prediction System

scholarly journals Neural Computation based General Disease Prediction Model

2019 ◽  
Vol 8 (2) ◽  
pp. 5646-5649 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Pattern Mining ◽  
Disease Prediction ◽  
Prediction System ◽  
Computing Technique ◽  
Soft Computing Technique ◽  
Artificial Neural ◽  
Prediction Techniques

To predict the patient disease using soft computing technique is the primary motto of the disease prediction system. Currently, researchers are trying to develop a disease prediction system using pattern mining technique. Here, a technique for disease prediction system using genetic algorithm and artificial neural network is proposed. The genetic algorithm is used for mining the most occurrences of disease sequences rules. To form the disease prediction system, the best rule which is obtained by means of genetic algorithm is used. Artificial neural network is trained to predict the disease. Accuracy of disease prediction is compared with other prediction techniques

An intelligent heart disease prediction system based on swarm-artificial neural network

2021 ◽  
Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Venki Balasubramanian ◽  
Varun G. Menon ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Artificial Neural

Automobile Engine Fault Diagnosis and Prediction System

2014 ◽  
Vol 1008-1009 ◽  
pp. 641-644
Author(s):  
Mei Lan Zhou ◽  
Ji Chang Wang ◽  
Yan Ping Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Fault Prediction ◽  
Prediction System ◽  
Framework Structure ◽  
Hardware Platform ◽  
Automobile Engine ◽  
Engine Vibration ◽  
Abnormal Sound

Aimed at the fault diagnosis and prediction of automobile engine, firstly designed a framework structure of automobile engine fault diagnosis and prediction system, and built a hardware platform; Secondly adopted the genetic algorithm neural network to fault prediction and diagnosis reasoning; Finally after analyzing automobile exhaust components, engine vibration, engine abnormal sound parameters, inferred the appeared and impending fault of automobile then made the tips for users on the screen. The results show that the performance of system is well, the accuracy of diagnosis and prediction is 95% in different conditions of experiment and debugging.

scholarly journals Survey on Heart Disease Anticipation Through Genetic Algorithm and BP-Neural Network

2021 ◽  
pp. 455-460
Author(s):  
Jaishri ◽  
Santosh Biradar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cardiovascular Disease ◽  
Heart Disease ◽  
Medical Diagnosis ◽  
Vital Role ◽  
Classification Problems ◽  
The Neural Network ◽  
Medical Diagnosis System ◽  
Good Set

Medical Diagnosis Systems play a vital role in medical practice and are used by medical practitioners for diagnosis and treatment. In this paper, a medical diagnosis system is presented for predicting the risk of cardiovascular disease. This system is built by combining the relative advantages of genetic algorithm and neural network. Multilayered feed forward neural networks are particularly suited to complex classification problems. The weights of the neural network are determined using genetic algorithm because it finds acceptably good set of weights in less number of iterations. The dataset provided by University of California, Irvine (UCI) machine learning repository is used for training and testing. It consists of 303 instances of heart disease data each having 14 attributes including the class label. First, the dataset is preprocessed in order to make them suitable for training. Genetic based neural network is used for training the system. The final weights of the neural network are stored in the weight base and are used for predicting the risk of cardiovascular disease. The classification accuracy obtained using this approach is 94.17%.

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