A Study on Machine Learning and Deep Learning Techniques Applied in Predicting Chronic Kidney Diseases

Chronic kidney disease (CKD) is one of the heterogeneous disorders in which the kidneys’ functionality degenerates over time. Although there is a range of abnormalities in kidney function, the malfunction going beyond a threshold leads to untreated kidney failure, also narrated as end-stage renal disorder. However, at times, high-end complex treatments such as kidney transplantation or dialysis may also be life-threatening in CKD patients. The situation often leads to irreversible kidney structure and function, which may also implicate cardio, endocrine, and xenobiotic toxic complications. CKD is identified as a decrease in GFR and/or a rise in albuminuria. As this health disorder becomes more prevalent, the quality of life index becomes detrimental. Moreover, the consequences impact the nation’s economy direct or indirectly. At this juncture, suitable preventive measures and strategic planning are imperative. On the other hand, the world is advancing with modern innovations. Artificial Intelligence, Machine Learning, and Deep Learning are unique technologies exhaustively employed in every sector. These disruptive technologies did not exempt the health segment and even proved their supremacy in several contexts. Accurate disease prediction and early detection are among the outcomes that could be expected from these technologies, so preventive measures could be suggested beforehand. In this article, a comprehensive investigation done by distinguished researchers is explored and presented. Around 100 articles published during the past decade are part of our study, which are deep-dived, and the respective contributions are cited.