Detecting Factors Responsible for Diabetes Prevalence in Nigeria using Social Media and Machine Learning
Bibliographic record
Abstract
Diabetes is a non-communicable disease associated with increased level of glucose due to inadequate supply of insulin (known as Type 1 diabetes) or inability to use insulin efficiently (known as Type 2 diabetes). Though the exact cause of Type 1 diabetes is unknown, the probable causes are genetics and environmental factors (such as exposure to viruses). On the other hand, Type 2 diabetes is largely linked to unhealthy lifestyle choices. In Nigeria, many people are believed to be living with diabetes and the country's diabetes prevalence rate is one of the highest in Africa. To determine the factors responsible for diabetes prevalence in Nigeria, we analyzed social media contents related to diabetes since billions of people, including diabetic patients and healthcare professionals, use social media platforms to freely share their experiences and discuss many health-related topics. None of the existing research targets the African audience who are also major users of social media platforms; hence our work aims to close this gap by leveraging an African social media platform targeted at Nigerians to gather diabetes-related data, and then applying machine learning technique to detect those factors responsible for diabetes prevalence in Nigeria. Based on our results, we discussed positive behavioural or lifestyle changes that are necessary to prevent and treat diabetes in Nigeria, as well as intervention designs required to bring about those changes. Future work will develop a diabetes intervention application implementing all the design features highlighted in Section V of this paper and making it generally accessible to Nigerians.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".