Abstract            Volume:10  Issue-2  Year-2022         Original Research Articles

Online ISSN : 2347 - 3215 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcret@gmail.com

Chronic Kidney Disease (CKD) refers to a great concern across the globe, with illness, humanity, and costs of healthcare involved at a very high level. Early recognition and prompt intervention are critical for the delay of CKD progression into end-stage renal disease (ESRD). However, presently available diagnostic methods are not successful in the early detection of CKD due to lack of continuous monitoring, which could play a vital role in CKD management. This paper handles an approach toward innovative healthcare solutions based on the Internet of Things (IoT) and advanced data analytic techniques to tackle the hitches in CKD diagnosis and management. Devices such as wearable sensors, glucose meters, and blood pressure monitors are non-invasive IoT devices for real-time monitoring of important health parameters and provide continuous information for early detection of CKD. This information can then be analyzed using various data analysis techniques, including machine learning and deep learning algorithms such as Convolutional Neural Networks (CNNs) and autoencoders, for the interpretation of large volumes of data produced by IoT devices to detect minute patterns reflecting the advancement of CKD. Such integration of IoT and data analytics can improve diagnostic certainty, facilitate adaptive treatment plans, and aid personalized healthcare approaches. Nonetheless, challenges on the way include data quality, real-time integration, and data privacy, yet the implications for how IoT and data analytics can change CKD diagnosis and management remain enormous to translate into enhanced patient outcomes and optimized healthcare delivery.

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