Abstract: The improvement in various medical facilities has allowed the improvement in the life of a lot of individuals. This has also exposed people that are susceptible to certain illnesses that affect one of the core organs of the human body. This has been one of the most serious medical issues that have put a pressure on the health-care system in recent years. This is a troubling development that has resulted in more complications and deaths as a result of kidney disease. Due to the fundamental nature of these diseases, which necessitates comprehensive testing and diagnosis, these kidney disorders are particularly difficult to detect. The delay in receiving the results causes a delay in delivering prompt treatment to the patient, which is critical for kidney illnesses, as failure to do so can end in a lot of pain for the patient or even death. Therefore, an automatic approach for the diagnosis is needed to achieve prompt kidney disease detection through the use of machine learning methodologies. This research article describes a precise kidney disease detection mechanism that utilizes K Nearest Neighbor and Pearson Correlation along with Artificial Neural Network and Decision Tree. The experimental results indicate a positive performance for the detection that is highly satisfactory.
Keywords— Kidney Disease Detection, K-Nearest Neighbors, Artificial Neural Network, Decision Tree.
|
DOI:
10.17148/IJARCCE.2021.10697
