Effective analysis of chronic kidney disease prediction using HRNN algorithm
DOI:
https://doi.org/10.21839/lsdjmr.2022.v1.20Keywords:
Chronic kidney disease, Machine learning, HRNN, IoTAbstract
Chronic Kidney Disease is a catch-all phrase for a variety of kidney illnesses. Chronic Renal Disease is another name for it. The illness affects 5 to 10% of the world's population. Chronic Kidney Disease is a worldwide health issue. Often these instances of Chronic Kidney Disease get it underdiagnosed or are eventually diagnosed in developing and underdeveloped countries; it is one of the main reasons why a greater percentage of these kinds of cases come from underdeveloped and developing countries as opposed to developed countries where most people have regular check-ups and diagnosis. Machine-learning systems could be used to identify Chronic Kidney Disease in what seems like a quick and accurate manner, allowing doctors to verify their test results in a relatively short period of time, going to allow a doctor to respond and recognise more sick people in less time than if he or She must go through the full diagnosis procedure by hand. Machine learning algorithms can be used for prediction, and exactness is determined by comparing various algorithms including such as Hybrid-Recurrent Neural Networks (HRNN). This method is used to forecast a dataset derived from a patient's medical history.
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