Comparative analysis of classification models for diagnosis Type 2 Diabetes
Bibliographic record
Abstract
Diabetes mellitus type 2 has become one of the major causes of premature diseases and death in many countries. It accounts for the majority of diabetes cases around the world. Thus, we need to develop a system that diagnoses type 2 diabetes. In this thesis, a fuzzy expert system is proposed using the Mamdani fuzzy inference system to diagnose type 2 diabetes effectively. In order to evaluate the performance of our system, a comparative study has been initiated, and will contrast the proposed system with data mining algorithms, namely J48 Decision tree, multilayer perceptron, support vector machine, and Naïve Bayes. The developed fuzzy expert system and the data mining algorithms are validated with real data from the UCI machine learning datasets. Moreover, the performance of the fuzzy expert system is evaluated by comparing it to related work that used the Mamdani inference system to diagnose the incidence of type 2 diabetes. Alternate title: Comparative analysis of data mining algorithms for diagnosis Type 2 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".