The comparison between multiple linear regression and machine learning methods in predicting cognitive function in Chinese type 2 diabetes
Bibliographic record
Abstract
Abstract The prevalence of type 2 diabetes (T2D) has been increasing drastically in recent decades. In the same time, it has been noted that dementia is related to T2D. In the past, traditional multiple linear regression (MLR) is the most commonly used method in analyzing these kinds of relationships. However, machine learning methods (Mach-L) have been emerged recently. These methods could capture non-linear relationships better than the MLR. In the present study, we enrolled old T2D and used four different Mach-L methods to analyze the relationships between risk factors and cognitive function. Our goals were first, to compare the accuracy between MLR and Mach-L in predicting cognitive function and second, to rank importance of the risks for impaired cognitive function in T2D. There were 197 old T2D enrolled (98 men and 99 women). Demographic and biochemistry data were used as independent variables and the cognitive function assessment (CFA) score was measured by Montreal Cognitive Assessment which was regarded as independent variable. In addition to traditional MLR, random forest (RF), stochastic gradient boosting (SGB), Naïve Byer’s classifier (NB) and eXtreme gradient boosting (XGBoost) were also applied. Our results showed that all the RF, SGB, NB and XGBoost outperformed than the MLR. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as the important factors from the more to the less important. In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than the MLR and in predicting CFA score. By these methods, the importance ranks of the risk factors are education level, age, frailty score, fasting plasma glucose and body mass index accordingly in a Chinese T2D cohort.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".