Predicting Kids Malnutrition Using Multilayer Perceptron with Stochastic Gradient Descent
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
The capability of predicting malnutrition kids is highly beneficial to take remedial actions on kids who are under 5 year’s age. In this article, Kid’s malnutrition predictive model is created and tested with our own collected dataset. We find the issues of kids malnutrition by the use of Machine Learning (ML) models. From ML-models, a multi-layer perceptron is used to classify the data neatly. Optimizing technique stochastic gradient descent (SGD) and Multilayer Perceptron (MLP) classifier methods are integrated to classify the data more effectively. To select the best features, from the feature selection (FS) technique filter-based method used. After selecting the best features, selected features are pass to the classifier model then the model will classify the data. Results with the MLP-SGD classifier were good than the other classifiers but after feature selection, the performance of the model was increased more. It will help in improving the analysis of malnutrition kid’s data. The sample data are collected from parents who are having kids less than five years of age at Repalle town, Andhra Pradesh, India.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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