Comparative analysis of classification models in diagnosis of type 2 diabetes
Bibliographic record
Abstract
Diabetes has become one of the major causes of premature diseases and death in most countries. A major problem in medical science and bioinformatics analysis is to obtain the correct diagnosis on specific important information. In general, several tests are done that includes clustering or classification on large scale of data. However, many tests might complicate the main diagnosis process and lead to difficulty in getting the final results. Machine learning techniques are used to build models to overcome this kind of difficulty. Therefore, there are two main purposes of this study. First, implementing classification models to diagnose diabetes efficiently and easily. Second, investigating and comparing the performance of different classification models. In this study, we proposed Fuzzy Expert System (FES) that used Fuzzy Inference System (FIS) model for incidence of diabetes. We applied two common classification algorithms which are logistic regression and support vector machine on Pima Indian Dataset to compare them with our proposed FIS model. In order to perform our experiment, we used two data mining tools named WEKA and MATLAB.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".