There are two main challenges when classifying large datasets, one is time consuming and the other is tedious work to label the more number of training samples. To maintain the high accuracy, it is necessary to generate a well defined training set. Kidney disease is a fast growing life-threatening disease. Kidney failure ends up in dialysis to keep the body normal. But the factors involved in disease progression and failure decide whether the patient needed to be placed on conservative care or dialysis. The major deciding factors included in this paper are depression, work type, and alcohol intake. In this paper, we use AFM-SVM to get better results. The newly introduced algorithm reduces the error rate and improves accuracy in the considered kidney disease dataset.
Volume 11 | 04-Special Issue
Pages: 466-473