Abstract: The rising number of kidney failure in adolescents and young children is of great concern. Pediatric CKD is a dynamic and complex medical and psychosocial disease with unique factors that separate this population from adults. Due to the unique and complex physical, psychological, and family backgrounds, young children may develop damage of kidneys. The long-term mortality for children, adolescents, and young adults with CKD (Chronic Kidney Disease) remains substantially higher than their healthy counterparts. The complex challenges that adolescent and young adult CKD patients face has to be dealt with on a serious note. Adolescents have different CKD etiologies and progress are quite dissimilar to that faced by adults, but have similar multifarious comorbidities. CKD can delay and limit growth. In this paper, various Machine Learning algorithms are used to predict the occurrence of the disease. The benefit of implementing this technique is that the disease can be diagnosed at an early stage based on the various symptoms of the patient and thus can help them to get the diagnosis and treatment on time which will lead to better health and better Quality of Life. Here, the prediction skill of several machine-learning algorithms for early prediction of CKD has been analyzed by usage of predictive analytics, in which the association of data parameters and the target class attributes is done. Predictive analytics enables us to introduce the optimal subset of parameters to feed machine learning to build a set of predictive models.

Keywords: Adolescents, Chronic Kidney Disorder, Machine Learning Algorithms.

PDF | DOI: 10.17148/IJARCCE.2020.9706

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