Determination of SWIR Features for Noninvasive Glucose Monitoring Using Machine Learning
Bibliographic record
Abstract
The use of infrared (IR) light for noninvasive glucose monitoring is a potential solution to reduce infection-related mortality rate for diabetic patients. However, IR spans a wide band and the relevant wavelengths need to be chosen. This paper presents an automated and computationally efficient model, capable of examining a large number of wavelengths, to determine the suitable ones for monitoring, based on feature selection and other machine learning techniques. The study examined wavelengths from 1300-2600nm which cover the majority of short-wave infrared (SWIR) band. For reliable ground truth, two datasets, D1 and D2, were used with 100 observations and 1000 observations respectively. In term of optimal performance with limited time and computational resources, Sequential Forward Floating Selection (SFFS) technique was chosen as a core feature selection algorithm due to its high accuracy and reasonable speed. Classifiers based on Support Vector Machine (SVM) were used to evaluate the performance of selected wavelengths. Principal Component Analysis (PCA) was used to enhance the accuracy. Pipeline and nested cross-validation techniques were adopted to prevent information leakage and biased results. The proposed approach managed to reduce the number of wavelengths by 65% for D1 and 58% for D2 while achieving accuracy and f1 score above 90%, which are 10% higher compared to other work in the literature. The feature selection results also suggest that suitable wavelengths fall in the range 1600-2600 nm.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".