An Enhanced Novel Dynamic Data Processing (ENDDP) Algorithm for Predicting Heart Disease in Machine Learning
DOI:
https://doi.org/10.32628/CSEIT206429Keywords:
Machine Learning, Massive Datasets, ENDDP.Abstract
Machine learning (ML) is a rapidly developing field in today's world. Use machine learning to extract data from a wide variety of sources. ML can solve various problems based on complex data sets. The prediction of heart disease is the most complex task in the medical field. It cannot be observed with the naked eye, it can appear immediately anywhere, anytime. Many ML algorithms are more capable of handling various algorithms. Due to complexity, the processing of massive data sets is more complicated. By improving these systems, the quality of medical diagnosis decisions can be improved. They can find patterns hidden in large amounts of data that will avoid the use of traditional statistical methods for analysis. In this article, An Enhanced New Dynamic Data Processing (ENDDP) Algorithm is developed to predict the early stages of heart disease. The results prove the performance of the proposed system.
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