Prediction of malnutrition in newbornInfants using machine learning techniques
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
<title>Abstract</title> This paper aims to predict malnutrition in newborn babies using various machine learning techniques. Malnutrition is characterized by the insufficient acquisition of fat and muscle mass during intrauterine growth. It is primarily caused by poor maternal nutrition and placental insufficiency, resulting in increased neonatal morbidity and mortality worldwide. In this study, we calculate the Z-score of newborns, taking into account factors such as age in months, weight, height, and sex, to determine the presence of malnutrition. The dataset utilized for this project is obtained from UNICEF for network training. The dataset is divided into two parts: one for validation and another for testing. We calculate WAZ (underweight) and LAZ (stunting) and train the models to detect neonatal malnutrition. Various machine learning models, including SVM, KNN, logistic regression, Naïve Bayes, and a two-layer neural network, are employed to identify malnutrition in children. Among these models, logistic regression demonstrates superior accuracy compared to the other algorithms.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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