Prediction of Postoperative Complication of Pediatric Cataracts Patients using Data Mining Methods (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> As a severe eye disease, pediatric cataracts threatens the visual development of children. The common treatment is replacing the cloudy lens with an artificial substitute via operation. However, patients may still suffer complications of visual deterioration within one year after surgery. Worse still, factors causing these complications are still unknown. </sec> <sec> <title>OBJECTIVE</title> This research adopts medical records from 321 patients as research material. We aim to apply data mining methods to predict postoperative complications of pediatric cataracts patients and explore which factors are related to these complications. </sec> <sec> <title>METHODS</title> First, we use random forest and naïve Bayesian classifier to predict the level of complication based on k-modes clustering towards the imbalanced datasets. Furthermore, genetic feature selection is exploited to find real features related to complications. In addition, apriori algorithm is employed to find the association rules whose consequent is complication to offer references for doctors. Finally, the relationship between the classification performance and the number of random forest tree is studied. </sec> <sec> <title>RESULTS</title> Average classification accuracies obtained in three binary classification problems (whether a patient suffers from complications, the first and second type of complication) are over 91%. Experimental results show that secondary IOL placement, operation mode, laterality, age at surgery, area of cataracts, density of cataracts, position of cataracts, nystagmus, microphthalmia, microcornea and persistent hyperplastic primary vitreous（PHPV）are related to complications. Except for the gender, operation mode and laterality, other attributes are related to the first type of complication. Except for the operation mode, laterality and PHPV attribute; other attributes are related to the second type of complication. </sec> <sec> <title>CONCLUSIONS</title> All experimental results shows the postoperative complication of pediatric cataracts patients can be predicted with the information of patients. Then the factors that are related to the complications are found. Finally, the association rules that is about the complications can provide reference to doctors. </sec>
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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) | 0.001 | 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 | 0.001 |
| 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 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".