High Precise Bipolar Disorder Detection Through Dynamic Laterality and Kernel Density Estimation Based Neural Networks
Bibliographic record
Abstract
The High-Precision Bipolar Disorder Detection through Dynamic Laterality and Kernel Density Estimation-based Neural Networks (HBDDKN) model offers a novel approach for classifying neurological and psychiatric disorders like bipolar disorder, schizophrenia, and epilepsy. Employing dynamic laterality analysis, kernel density estimation, and neural network learning, HBDDKN processes Functional MRI (fMRI) and EEG data, calculates dynamic laterality indices, estimates probability distributions, and implements embedding techniques to enhance data representation. A fuzzy mapping method determined optimal hidden layer centres, and a radial basis function (RBF) improved the modelling of non-linear relationships. A bidirectional LSTM (BiLSTM) architecture optimised the network for sequential dependencies in EEG signals. The following parameters are calculated using the model performance of HBDDKN, the confusion matrices of HBDDKN, the comparative ROC analysis, and the comparative radar plot for bipolar disorder.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".