Machine learning and IoT‐based model for patient monitoring and early prediction of diabetes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary Health monitoring is one of the sustainable development areas throughout the globe and Diabetes Mellitus is a common disease worldwide that is one of the main causes of health disasters. Currently, Internet of Things (IoT) and machine learning (ML) technology together provide a proficient approach for monitoring and predicting diabetes mellitus. In this article, we have proposed a model which uses the hybrid enhanced adaptive data rate (HEADR) algorithm for long range (LoRa) protocol of the Internet of Things (IoT) for patient's real‐time data gathering. Further, machine learning prediction takes place by using classification methods for the detection of diabetes severity levels on collected data through LoRa protocol. The performance of the LoRa protocol is evaluated on the Contiki Cooja simulator based on throughput and packet collision parameters. The proposed model uses different machine learning classifiers, namely, gradient boosting (GB), random forest (RF), decision tree (DT), support vector machine (SVM), K‐nearest neighbors (KNN), logistic regression (LR), and Gaussian Naive Bayes (GNB) to predict diabetes with maximum accuracy score, precision, recall, F‐measure, and receiver operating curve (ROC), using Python programming language.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it