Improving Depression Prediction Accuracy Using Fisher Score-Based Feature Selection and Dynamic Ensemble Selection Approach Based on Acoustic Features of Speech
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Bibliographic record
Abstract
Depression affects over 322 million people, and it is the most common source of disability worldwide. Literature in speech processing revealed that speech could be used for detecting depression. Depressed individuals exhibit varied acoustic characteristics compared to non-depressed. A four-staged machine learning classification system is developed to investigate the acoustic parameters to detect depression. Stage one uses speech recordings from a publicly available and clinically validated dataset DAIC-WOZ. The baseline acoustic feature vector, eGeMAPS, is extracted from the dataset in stage two. Adaptive synthetic (ADASYN) is performed along with data preprocessing to overcome the class imbalance. In stage three, we conducted feature selection (FS) using three techniques; Boruta FS, recursive feature elimination using support vector machine (SVM-RFE), and the fisher score-based FS. Experimentation with various machine learning base classifiers like gaussian naïve bayes (GNB), support vector machine (SVM), k-nearest neighbors (KNN), logistic regression (LR), and random forest classifier (RF) is performed in stage four. The hyperparameters of the classifiers are tuned using the GridSearchCV technique throughout the 10-fold stratified cross-validation (CV). Then we employed multiple dynamic ensemble selection of classifier algorithms (DES) with k=3 and k=5 utilizing the pool of aforementioned four base classifiers to improve the accuracy. We present a comparative study using eGeMAPS features against the base classifiers and the experimented DES classifiers. Our results on the DAIC-WOZ benchmark dataset suggested that K-Nearest Oracles Union (KNORA-U) DES with k=3 has superior accuracy using a subset of 15 features selected by fisher score-based FS than the individual base classifiers.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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 it