Comparison of a Custom Artificial Intelligence Model with Classic Models in Alzheimer’s Disease Diagnosis
Fragment książki (Materiały konferencyjne)
MNiSW
20
konferencja
| Status: | |
| Autorzy: | Tokarski Piotr, Łazaruk Karol, Czapla Krystian, Czarnecki Dawid, Dzieńkowski Mariusz |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 272 - 283 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus | Google Scholar | DBLP |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 27th International Conference on Engineering Applications of Neural Networks |
| Skrócona nazwa konferencji: | 27th EANN 2026 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 16 lipca 2026 do 19 lipca 2026 |
| Miasto konferencji: | Chania, Creta |
| Państwo konferencji: | GRECJA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| With the significant development of artificial intelligence (AI) in various fields, including medicine, many processes that were previously somewhat limited have undergone streamlining. In medicine, artificial intelligence has supported the diagnosis of various diseases and disorders, enabling early detection and facilitating a faster treatment process and recovery from disease. In this article, an authorial artificial intelligence model was created based on a dataset on Alzheimer’s disease (AD), the cause of which is difficult to identify, and then its prediction results were compared with classic AI models to see whether a model explicitly created for a specific case would achieve better results than models with a wide range of applications. The research showed that existing classic models can easily adapt to new cases and, in a short time, prepare a well-specialized model with a high probability of accurate prediction. Additionally, selecting an appropriate model for the dataset’s size proved to be a crucial aspect. The author’s model achieved high results. |
