Designing a Smart IoT Environment by Predicting Chronic Kidney Disease Using Kernel Based Xception Deep Learning Model
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".