Optimized Neural Networks for Diabetes Classification Using Pima Indians Diabetes Database
Bibliographic record
Abstract
Integrating artificial intelligence (AI) into the healthcare sector holds immense potential for transforming the industry, promising notable improvements in diagnostic precision, treatment effectiveness, and overall patient care. This paper explores the detection of diabetes using two types of neural networks - feedforward neural network (FNN) and convolutional neural network (CNN) - with the Pima Indians diabetes database (PIDD). To evaluate the efficiency of the proposed models in diagnosing diabetes, various essential metrics are utilized, including accuracy, precision, recall, F1-score, specificity, receiver operating characteristic - area under the curve (ROC-AUC), log loss, false positive rate (FPR), Youden's index, and Matthews correlation coefficient (MCC). The proposed FNN model achieves an impressive accuracy rate of 82%, outperforming previous methodologies, whereas the CNN displays commendable accuracy of 80.52%. Both models demonstrate superb performance in terms of accuracy, specificity, and AUC, highlighting their effectiveness in binary classification when compared to prior studies. This research provides valuable insights into utilizing advanced machine-learning techniques for the early detection of diabetes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".