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

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Soft Computing and Signal Processing ( ICSCSP 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 840))

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India

    Kalyani Chapa & Bhramaramba Ravi

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  1. Kalyani Chapa
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  2. Bhramaramba Ravi
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Correspondence to Kalyani Chapa .

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  1. Faculty of Science and Technology, International Institute of Advance Technology (ICATSUC), Sarawak, Malaysia

    Hushairi Zen

  2. Department of Computer Science, University of South Australia, Adelaide, SA, Australia

    Naga M. Dasari

  3. Department of Electronics and Communication Engineering, Malla Reddy Engineering College for Women, Secunderabad, India

    Y. Madhavee Latha

  4. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, India

    S. Srinivasa Rao

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Chapa, K., Ravi, B. (2024). A Study on Machine Learning and Deep Learning Techniques Applied in Predicting Chronic Kidney Diseases. In: Zen, H., Dasari, N.M., Latha, Y.M., Rao, S.S. (eds) Soft Computing and Signal Processing. ICSCSP 2023. Lecture Notes in Networks and Systems, vol 840. Springer, Singapore. https://doi.org/10.1007/978-981-99-8451-0_7

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