A hybrid BiLSTM and rule-based system for integrated diabetes prediction and personalized guidance
Muhammad Saleem, Muhammad Hamid, Saadia Malik · 2026
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Description
Muhammad Saleem and colleagues describe a 2026 hybrid system for diabetes care. Diabetes is a long-term disease of high blood sugar. Many prediction tools stop at a risk score and give no daily guidance, so the team paired prediction with personal advice.
One part is a neural network that reads data in both directions to spot patterns over time. The other part is a rule engine that turns a result into lifestyle tips. The team merged two diabetes datasets and checked the system with ten-fold testing, which splits data in ten parts in turn. It scored about eighty-four percent accuracy overall. The pairing aims to link a diagnosis to day-to-day self-care.
Catalog record
| CREATOR | Muhammad Saleem, Muhammad Hamid, Saadia Malik |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 34 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |