Designing a Smart IoT Environment by Predicting Chronic Kidney Disease Using Kernel Based Xception Deep Learning Model

Abstract

Chronic Kidney Disease (CKD) is often asymptomatic in its early stages, and patients may not experience noticeable symptoms until the disease has significantly progressed. This challenge in early detection results in patients seeking medical attention only when complications arise. Symptoms, when present, are nonspecific and vary widely among individuals, including fatigue, swelling, and changes in urination patterns, which may be mistakenly attributed to other conditions, leading to delayed diagnosis. In contemporary healthcare applications, the integration of Cloud Computing (CC) and the Internet of Things (IoT) has become commonplace. The cloud, with its superior processing capability compared to mobile devices, is particularly advantageous in analyzing the vast volumes of patient data generated by IoT devices. Machine Learning (ML) and Deep Learning (DL) models have gained interest in medical diagnostics due to their excellent prediction accuracy. This research introduces a novel method for diagnosing CKD using IoT and Cloud Computing. The selection of appropriate features and algorithms is crucial for optimizing the final model's performance. To address missing values and enhance results, a unique sequential approach is employed. Furthermore, the classification step utilizes m-Xception, employing a distinct architecture and breaking down the convolution layer into depth-based sub-layers linked by linear residuals. Effective model training results from a well-defined learning strategy. For selecting model kernel values, especially in large-scale examples, a Squeaky Wheel Optimization (SWO) metaheuristic is recommended. The projected model undergoes simulation testing on the canonical CKD dataset and is statistically evaluated. The findings suggest the feasibility of developing an automated method for estimating CKD severity. In conclusion, recent advances in predictive modeling and deep learning offer a fresh perspective on problem-solving, with potential applications in the field of renal illness and beyond.