Use of Modular Neural Network for Heart Disease

The medical field is very versatile field and one of the interested research areas for the scientist. It deals with many medical disease problems starting with the diagnosis of the disease, preventing from the disease and treatment for the disease. There are various types of medical disease and accordingly various types of treatment methods. In this paper we mostly concern about the diagnosis of the heart disease. Mainly two types of the diagnosis method are used one is manual and other is automatic diagnosis which consists of diagnosis of disease with the help of intelligent expert system. In this paper the modular neural network is used to diagnosis the heart disease. The attributes are divided and given to the two neural network models Backpropagation Neural Network (BPNN) and Radial Basis Function Neural Network (RBFNN) for training and testing. The two integration techniques are used two integrate the results and provide the final training accuracy and testing accuracy. The modular neural network with probabilistic product method gave an accuracy of 87.02% over training data and 85.88% over testing accuracy and with probabilistic product method gave an accuracy of 89.72% over training data and 84.70% over testing accuracy, which was experimentally determined to be better than monolithic neural networks

Early Prediction of Diabetes Using Deep Learning Convolution Neural Network and Harris Hawks Optimization

 Owing to the gravity of the diabetic disease the minimal level symptoms for diabetic failure in the early stage must be forecasted. The prediction system instantaneous and prior must thus be developed to eliminate serious medical factors. Information gathered from Pima Indian Diabetic dataset are synthesized through a profound learning approach that provides features for diabetic level information. Metadata is used to enhance the recognition process for the profound learned features. The distinct details retrieved by integrated machine and computer technology, including glucose level, health information, age, insulin level, etc. Due to the efficacious Hawks Optimization Algorithm (HOA), the data's insignificant participation in diabetic diagnostic processes is minimized in process analysis luminosity. Diabetic disease has been categorized with Deep Learning Convolution Networks (DLCNN) from among the chosen diabetic characteristics. The process output developed is measured on the basis of test results in terms of error rate, sensitivity, specificity and accuracy