Reliable and Systematic Diabetes Prediction Based on Health Indicators using Machine Learning Algorithm
Bibliographic record
Abstract
Early and accurate prediction of diabetes is one of the dire objectives of medical diagnostics. This paper contains a comparative work of the K-Nearest Neighbor (KNN) and Decision Tree Classifier (DTC) in predicting diabetes based on clinical health indicators. A critical preprocessing of the dataset was done taking care of missing values, feature scaling, and normalization to guarantee the integrity and soundness of the models. Each of these two algorithms was run 10 times to check the consistency and performance. As the performance measure showed, KNN seemed to give better results than DTC with an average accuracy of around 92.5 percent, whereas Decision Tree got 90.0 percent. The worst accuracy achieved on experimentation with KNN was 87.5 which shows consistency in prediction ability. Besides, an average score of 0.91 of KNN compared to 0.80 of DTC highlights the use of KNN as a more precise and robust algorithm to work with the standardized data. Standard error bars also indicate a less varying KNN results. The conclusion of this comparative study is that both models are suitable to use in classifying diabetes but KNN is more reliable and accurate and therefore is a good option to use in any situation that involves high performance diagnosis using health indicators as the input. The results of the study could help practitioners and developers to select the proper machine learning models to integrate with clinical decision support systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".