Bibliographic record
Abstract
Early and correct diagnosis is still a major difficulty in today’s healthcare system, especially in impoverished and distant areas with limited access to professional medical staff and testing facilities. worse treatment expenses and worse death rates are frequently the results of delayed diagnosis. Using cutting-edge Deep Learning (DL) and Machine Learning (ML) architectures combined with edge computing for real-time deployment, this study provides a reliable, scalable, and multi-modal disease detection system. The suggested system can identify more than 50 diseases by using ensemble learning techniques for structured clinical data and Convolutional Neural Networks (CNNs) for image-based diagnosis. These include infectious diseases like sickle cell anemia and malaria, complicated pathologies like brain tumors and different types of cancer, and chronic problems like diabetes and cardiovascular disorders.Offline inference in low-bandwidth contexts is made possible by the system’s deployment optimization on the NVIDIA Jetson Nano. With diabetes prediction at 94%, obesity classification at 99%, brain tumor detection at 96%, and chronic kidney disease prediction at 99%, experimental results show remarkable predictive accuracy across different models. This approach offers an affordable answer to global health issues by bridging the gap between cutting-edge AI capabilities and easily accessible healthcare infrastructure.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".