Performance Assessment of Decision Tree-Based Predictive Classifiers for Risk Pregnancy Care
Bibliographic record
Abstract
The e-Health core concept includes Web usage in an integrated way with tools and services for healthcare. This definition improves access, efficiency, and clinical care quality process that are necessary for a service delivery improvement. Decision support systems (DSSs) belong to a plethora of e-Health concept dimensions. For these systems construction, it is important to find a reliable intelligent mechanism capable to identify diseases that can worsen the patient's clinical condition. Thus, this paper proposes the use of tree-based data mining (DM) techniques for the hypertensive disorders prediction in the risk gestation. It presents the modeling, performance evaluation, and comparison between the tree based classifiers ID3 and NBTree. The 5-fold cross-validation method realizes the performance comparison. Results show that the NBTree classifier obtained better performance, presenting F-measure 0.609, ROC area 0.753, and Kappa statistic 0.4658. This classifier can be a key to a smart system development capable to predict risk events in pregnancy. Therefore, DSSs are a leading solution for the reduction of both mother and fetal mortality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".