… and raise the awareness of the disease spread. It had been … , 2020): The K-Means methods
were employed in this paper for … of this paper propose a neural network of K-Means-LSTM to …

… the different approaches in data mining. The author also compares the results for single as
well as for the hybrid approach in data mining with k means, bayes, and neural network which …

… for heart disease treatment which leads to towards more accuracy. Among them … hybrid
approach of k-means and Artificial neural networks provides a high accuracy prediction …

… heart disease prediction using IoT and cloud based system. Malav et al. [11] used unsupervised
learning method like K-Means and Artificial Neural Network … A hybrid method using fuzzy …

… through the K-Means clustering algorithm for the removal of duplicate data, and then, the …
a systematic approach for the prediction of heart disease using deep neural networks (DNNs…

… predict heart disease earlier by utilizing attribute extraction techniques. Several techniques in
Artificial Neural Network (… A k-means clustering algorithm has been used by researchers [27…

… the algorithms Decision Tree, KNN and K-Means that can be used for … an artificial neural
network. The framework was built in the … An accuracy of 96 percent is shown by Hybrid methods. …

… to predict heart disease in a big data environment. The … k-means initialization method with
classification algorithms for the enhancement of heart stroke severity prediction, without using …

… mean is calculated through k-means clustering algorithm. The … prediction of heart disease
risk using data mining techniques of support vector machine and artificial neural network…

… effects such as heart disease, stroke, renal failure, and high … of the disease’s consequences.
This study aims to develop a … A hybrid prediction model for type 2 diabetes using K-means …