Cargando…
Rule extraction from biased random forest and fuzzy support vector machine for early diagnosis of diabetes
Due to concealed initial symptoms, many diabetic patients are not diagnosed in time, which delays treatment. Machine learning methods have been applied to increase the diagnosis rate, but most of them are black boxes lacking interpretability. Rule extraction is usually used to turn on the black box....
Autores principales: | Hao, Jingwei, Luo, Senlin, Pan, Limin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198101/ https://www.ncbi.nlm.nih.gov/pubmed/35701587 http://dx.doi.org/10.1038/s41598-022-14143-8 |
Ejemplares similares
-
Fuzzy support vector machine: an efficient rule-based classification technique for microarrays
por: Hajiloo, Mohsen, et al.
Publicado: (2013) -
Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image
por: Zhong, Xiaomei, et al.
Publicado: (2013) -
Time-Shift Multiscale Fuzzy Entropy and Laplacian Support Vector Machine Based Rolling Bearing Fault Diagnosis
por: Zhu, Xiaolong, et al.
Publicado: (2018) -
Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine
por: Wang, Ke-Fan, et al.
Publicado: (2022) -
Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis
por: Tun, Wunna, et al.
Publicado: (2021)