Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

Title:Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease

Volume: 14 Issue: 8

Author(s): Pramila Arulanthu*Eswaran Perumal

Affiliation:

• Department of Computer Applications, Alagappa University, Karaikudi,India

Keywords: Chronic Kidney Disease (CKD), Fuzzy, Data Mining, Classification, Prediction, Global Burden of Disease (GBD).

Abstract: The data generated by medical equipment is huge and humongous in size loaded with valuable information. This data set requires an effective classification for accurate prediction. The prediction of health issues is an extremely difficult task, especially Chronic Kidney Disease (CKD) is one of the major unpredictable diseases in the medical field. Perhaps, certain medical experts do not have identical awareness and skills to solve the issues of their patients. Most of the medical experts may have unsubstantiated results on disease diagnosis of their patients. Sometimes, patients may lose their life owing to disease severity. As per the Global Burden of Disease (GBD-2015) report, death by CKD was ranked at 17th position whereas GBD-2010 reported the same at 27th among the causes of death globally. Death by CKD constitutes 2.9% of all deaths between the years 2010 and 2013 among people in the age range of 15 to 69. As per the World Health Organization (WHO- 2005) report, CKD was the primary reason behind the death of 58 million people so far. Hence, this article presents a state-of-the-art review of the classification and prediction of Chronic Kidney Disease (CKD). Normally, advanced data mining techniques, fuzzy and machine learning algorithms are used to classify medical data and disease diagnosis. This study reviews and summarizes many classification techniques and disease diagnosis methods presented earlier. The main intention of this review is to point out and address some of the issues and complications of the existing methods. It also attempts to discuss the limitations and accuracy level of the existing CKD classification and disease diagnosis methods.

Cite this article as:

Arulanthu Pramila *, Perumal Eswaran , Risk Factor Identification, Classification and Prediction Summary of Chronic Kidney Disease, Recent Advances in Computer Science and Communications 2021; 14 (8) . https://dx.doi.org/10.2174/2666255813666200101100424