Tackling No-show Imbalance problems for Healthcare Appointment datasets
Bibliographic record
Abstract
Medical appointment no-shows have a significant impact on the revenue, cost and resource utilization for almost all healthcare systems. To address this problem, numerous efforts have been made in the past to apply machine learning algorithms for predicting patient no-shows. However, the issue of class imbalance that often arises in these cases has largely been neglected. Given the highly imbalanced nature of our data, we propose a novel ensemble method for classification that generates an arbitrary number of balanced splits of the data. This method uses Instance Hardness as a weighting mechanism to create balanced bags, with each bag containing all minority instances and a filtered subset of majority instances. We then employ the Histogram-based Gradient Boosting classifier as the base learner for each bag. This approach allows the base learners to train on different balanced bags, each reflecting varied characteristics of the training data. We tested our proposed method on four no-show datasets, and the results demonstrate a substantial improvement in classification performance compared to other methods.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 | 0.001 |
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 teacher head, 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".