Predictive Analytics Based on the NHANES 1999-2016 Dataset for the Hepatitis an Antibody Prediction: A Python Case Study
Bibliographic record
Abstract
Predictive analytics aims at building an analytical model in order to predict a target variable.This data science area currently has a lot of applications in many fields, such as in analytical customer relationship management, direct marketing, project risk management, clinical decision support systems, etc.Our research aims at performing predictive analytics on healthcare dataset to search for potentially valuable prediction models that are able to predict health-related target variables based on related input factors such as demographics, diet habit and relevant examination factors such as weight and height, etc.The healthcare data that have been used for our predictive analysis is collected from an important program conducted by the U.S. Centers for Disease Control and Prevention, which consists of 93,702 observations across 961 categories containing both interview and examination data from more than 93,000 participants.We have employed Multi-Linear Regression, Logistic Regression, Support Vector Classification, Support Vector Regression, Random Forest Classification (RFC), and Random Forest Regression algorithms to build various prediction models on the cleaned dataset.The result has shown that we have achieved good models based on the prediction of related social and healthcare factors (AUC ranging from 0.76 to 0.87), RFC has outperformed other classification algorithms, Fisher Score was a key feature selection algorithm, and the demographical factors have played a dominant role in the prediction of some questionnaire and laboratory target variables.Finally, based on the result of the best prediction models, we decided to develop a Hepatitis A Antibody prediction web prediction system.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".