Kidney Diseases Patient Healthcare Monitoring using AI-Driven-IoT(AIIoT) - An KSK1 Approach

Abstract

Monitoring kidney disorders with the assistance of trained medical professionals are one of the most important aspects of managing and treating these conditions. Imaging investigations, blood tests, urine tests, blood pressure monitoring, and routine check-ups are critical for tracking the evolution of the condition and making any required changes to treatment plans. All of these are essential components. Patients with renal abnormalities can enhance their quality of life and lower their risk of developing complications if they receive proper and timely monitoring. This is due to their enhanced abilities to manage their conditions. The KSK1 strategy, which integrates IoT and artificial intelligence, is transforming decision-making. This improves data accuracy and reliability, enabling real-time decision-making, and makes the solution more cost-effective. However, in order to fully realise the promise of this method, businesses must identify and resolve the challenges that arise while utilising it. With the continued development of artificial intelligence (AI) and the internet of things (IoT), the KSK1 method has enormous potential to revolutionise decision-making procedures and create a more intelligent and efficient environment. This model was designed expressly to meet the criteria of the work being offered. These classifiers are used to classify disease datasets, notably in sectors such as renal disease datasets. Three main indicators are used to judge how effectively the classifiers function. It is critical to understand that the measurements under discussion here are accuracy, precision, and recall. If the suggested KSK1 approach is used, an accuracy rate of 87% to 95% can be attained for each and every disease. Accuracy of 92.5%, precision of 94.5%, recall of 94.5%.

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