… the BF network of probabilistic artificial neural networks. This … Then we worked on hybrid by
combining K Means with naive … to predict heart disease and then analyze the best method of …

… A hybrid method, combining evolutionary algorithms and support vector machines for … (C-BiLSTM),
which uses the K-Means clustering approach to remove duplicate data. According to …

… methods such as neural network (NN), deep neural network (… methods, such as K-Means
and K-Medoids, under uncertain … for the automated diagnosis of congestive heart failure using …

… Very good PNN prediction ability results are obtained based on the form of the environment
… Proposed approach At this point, is necessary to highlight how k-means clustering is used in …

… in diagnosing heart disease well in advance. This paper looked at the early-stage
decision-making strategy for heart disease using Decision Tree, k-means, SVM and neural network. …

… machine (SVM), a deep neural network (DNN), decision tree (DT), … output data in the prediction
form, using the k-means and NB … Hybrid approach for heart disease prediction using data …

… by hybrid approach which was better than all other approaches. … used Deep Neural Network
(DNN) to avoid underfitting and … A hybrid approach (2020) to predict heart disease was …

… -Fuzzy Neural Network and K-means clustering - SVM classifier. Inspired of this, to predict
the heart failure rate, a hybrid Genetic Algorithm and neural network was used to reform the …

… heart disease prediction is displayed in Fig. 1. An enhanced k-means clustering (IKC) method
… feature subclass and feeding it into the neural network for training. Different techniques for …

… a method for predicting and diagnosing heart disease. They have performed data mining
techniques utilizing artificial … In this work, k-means clustering is used to locate and merge such …