An Optimised Multivariable Regression Model for Predictive Analysis of Diabetic Disease Progression
Bibliographic record
Abstract
With the advent of smart systems and smart IoT network all over the world leading to enormous amount of data generation; the right analysis and decision making based on the relevant data plays a crucial role. Various industries such as transportation, retail, healthcare etc. rely on analysis using this huge volumes of data for intelligent decision making. In smart healthcare system, accurate analysis of patients' data and prediction of diseases and medicine is important. To a great extent, fatalities can be avoided by timely recommendation of healthcare measures and immediate alert on emergency conditions. The use of machine learning algorithm for precise predictive analysis of data can be very promising in the field of healthcare. In this paper, optimised Multivariable Linear regression method is used to predict the diabetic disease progression of 442 patients based on various parameters such as age, gender, Body Mass Index and 6 different blood serum measurements. Here optimisation is performed using feature reduction and logarithmic transformation. The predicted output is found to be closely associated with actual output data with a Root Mean Square Error of 1.5 units; which indicates higher accuracy in comparison with the non-optimised model with the error of 54 units. There has also been a comparison with the results obtained from other state of the art regression methods, which proves that the proposed model exhibits maximum accuracy. This method can be used to provide promising medical advice to the patients on how to reduce the diabetic disease progression over a year by controlling various health parameters.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".