Výsledky vyhledávání - Predictive Learning Models classification

Photoreceptor layer thinning is an early biomarker for type 2 diabetes: a cohort study in UK Biobank.

Autoři: Ma Y Wu Y Hu L a další

Zdroj: Eye (London, England) [Eye (Lond)] 2025 Dec; Vol. 39 (17), pp. 3135-3141. Date of Electronic Publication: 2025 Oct 03.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 8703986 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-5454 (Electronic) Linking ISSN: 0950222X NLM ISO Abbreviation: Eye (Lond) Subsets: MEDLINE

Výrazy ze slovníku MeSH: Diabetes Mellitus, Type 2*/diagnostic imaging , Diabetes Mellitus, Type 2*/epidemiology , Diabetes Mellitus, Type 2*/etiology , Predictive Learning Models*/classification , Predictive Learning Models*/statistics & numerical data , Tomography, Optical Coherence*/classification , Tomography, Optical Coherence*/statistics & numerical data , Retinal Photoreceptor Cell Outer Segment*/pathology, Proportional Hazards Models ; UK Biobank ; United Kingdom/epidemiology ; Biomarkers/analysis ; Humans ; Male ; Female ; Middle Aged ; Follow-Up Studies ; Prospective Studies ; Predictive Value of Tests ; Risk Factors ; Risk Assessment/methods

Deep learning for predicting myopia severity classification method.

Autoři: Xing W Li X Ni J a další

Zdroj: Biomedical engineering online [Biomed Eng Online] 2025 Jul 09; Vol. 24 (1), pp. 85. Date of Electronic Publication: 2025 Jul 09.

Způsob vydávání: Evaluation Study; Journal Article

Informace o časopise: Publisher: BioMed Central Country of Publication: England NLM ID: 101147518 Publication Model: Electronic Cited Medium: Internet ISSN: 1475-925X (Electronic) Linking ISSN: 1475925X NLM ISO Abbreviation: Biomed Eng Online Subsets: MEDLINE

Výrazy ze slovníku MeSH: Image Processing, Computer-Assisted*/methods , Deep Learning* , Myopia*/classification , Myopia*/diagnostic imaging , Severity of Illness Index* , Predictive Learning Models*, Humans ; Fundus Oculi ; Reproducibility of Results ; Retina/diagnostic imaging

Accurate and Efficient Fluid Flow Regime Classification Using Localized Texture Descriptors and Machine Learning

Autoři: Renganathan, Manimaran Krishnan, Palani Thanaraj Columbus, C. Christopher a další

Zdroj: IEEE Access. 13

Témata: Feature extraction, Computational modeling, Fluids, Accuracy, Predictive models, Computational fluid dynamics, Atmospheric modeling, Analytical models, Deep learning, Data models, Image processing, flow classification, computational fluid dynamics, low and high-speed flow, local binary pattern

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-48065
https://doi.org/10.1109/access.2025.3594850

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

Autoři: Chau HC Liu JYH Rudd JA

Zdroj: Scientific reports [Sci Rep] 2025 Apr 16; Vol. 15 (1), pp. 13105. Date of Electronic Publication: 2025 Apr 16.

Způsob vydávání: Evaluation Study; Journal Article; Validation Study

Informace o časopise: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Výrazy ze slovníku MeSH: Databases, Pharmaceutical* , Deep Learning* , Electrodiagnosis*/classification , Drug-Related Side Effects and Adverse Reactions*/classification , Digestive System Diseases*/chemically induced , Digestive System Diseases*/diagnosis , Predictive Learning Models*, Big Data ; Humans ; Feasibility Studies ; Gastrointestinal Tract/drug effects ; Nausea/chemically induced ; Nausea/diagnosis ; Vomiting/chemically induced ; Vomiting/diagnosis ; Diarrhea/chemically induced ; Diarrhea/diagnosis ; Constipation/chemically induced ; Constipation/diagnosis ; Biological Clocks/drug effects ; Area Under Curve ; ROC Curve ; Reproducibility of Results

Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

Autoři: Guo J Bu R Shen W a další

Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2025 Jun; Vol. 265, pp. 108759. Date of Electronic Publication: 2025 Mar 30.

Způsob vydávání: Evaluation Study; Journal Article

Informace o časopise: Publisher: Elsevier Scientific Publishers Country of Publication: Ireland NLM ID: 8506513 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-7565 (Electronic) Linking ISSN: 01692607 NLM ISO Abbreviation: Comput Methods Programs Biomed Subsets: MEDLINE

Výrazy ze slovníku MeSH: Liver Neoplasms*/classification , Liver Neoplasms*/diagnostic imaging , Ultrasonography*/classification , Ultrasonography*/methods , Image Interpretation, Computer-Assisted*/methods , Multimodal Imaging*/classification , Multimodal Imaging*/methods , Predictive Learning Models*, Humans ; Classification Algorithms ; Image Processing, Computer-Assisted/classification ; Image Processing, Computer-Assisted/methods ; Area Under Curve ; Data Accuracy ; Male ; Female

Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.

Autoři: Minotti M Negrini S Cina A a další

Zdroj: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society [Eur Spine J] 2024 Nov; Vol. 33 (11), pp. 4164-4170. Date of Electronic Publication: 2023 Dec 06.

Způsob vydávání: Journal Article; Multicenter Study; Observational Study

Informace o časopise: Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 9301980 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0932 (Electronic) Linking ISSN: 09406719 NLM ISO Abbreviation: Eur Spine J Subsets: MEDLINE

Výrazy ze slovníku MeSH: Deep Learning* , Scoliosis*/classification , Scoliosis*/diagnostic imaging , Spine*/diagnostic imaging , Spine*/pathology , Photogrammetry*/methods , Photogrammetry*/statistics & numerical data , Predictive Learning Models* , Anthropometry*/methods, Humans ; Male ; Child ; Adolescent ; Young Adult ; Cross-Sectional Studies ; Clinical Studies as Topic ; Severity of Illness Index ; Predictive Value of Tests ; Radiography/methods ; Radiography/statistics & numerical data ; Reproducibility of Results

Noise robust classification of carbide tool wear in machining mild steel using texture extraction based transfer learning approach for predictive maintenance

Autoři: Sekhar, Ravi Pandya, Sharnil, Researcher Shah, Pritesh a další

Zdroj: Results in Control and Optimization. 17

Témata: Acoustics, Deep learning, Predictive maintenance, Smart manufacturing, Sound classification, Texture extraction, Tool condition monitoring, Transfer learning, Maskinteknik, Mechanical Engineering

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-138340
https://doi.org/10.1016/j.rico.2024.100491

Predictive models for road traffic sign: Retroreflectivity status, retroreflectivity coefficient, and lifespan

Autoři: Saleh, Roxan Fleyeh, Hasan, Associate professor

Zdroj: International Journal of Transportation Science and Technology. 16:276-291

Témata: Daylight Chromaticity, Machine learning algorithms, Prediction, Retroreflectivity, Road signs, Highway administration, Highway planning, Learning algorithms, Logistic regression, Machine learning, Motor transportation, Roads and streets, Traffic signs, Croatia, High-accuracy, Lifespans, Predictive models, Road traffic, Warranty period, Forecasting

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:du-48218
https://doi.org/10.1016/j.ijtst.2024.02.008

Data-Driven Prediction of Student Success in Higher Education: A Predictive Modeling Study of Academic Outcomes via Binary Classification

Autoři: Fortunato, Maria Carlota De Almeida Fernandes Santos, Augusto José Rabelo Almeida RUN

Přispěvatelé: Fortunato, Maria Carlota De Almeida Fernandes Santos, Augusto José Rabelo Almeida RUN

Témata: Student Success Prediction, Machine Learning, Educational Data Mining, Early Warning Systems, Higher Education Analytics, Academic Dropout, SDG 4 - Quality educa, SDG 8 - Decent work and economic growth, SDG 9 - Industry, innovation and infrastructure, SDG 10 - Reduced inequalities, Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação

Popis souboru: application/pdf

Dostupnost: http://hdl.handle.net/10362/190667

Optimizing Machine Learning Methods to Improve Predictive Models of Alzheimer’s Disease

Autoři: Ezzati, Ali Zammit, Andrea R Harvey, Danielle J a další

Zdroj: Journal of Alzheimer’s Disease. 71(3)

Témata: 32 Biomedical and Clinical Sciences (for-2020), 5202 Biological Psychology (for-2020), 3202 Clinical Sciences (for-2020), 3209 Neurosciences (for-2020), 52 Psychology (for-2020), Bioengineering (rcdc), Aging (rcdc), Neurodegenerative (rcdc), Dementia (rcdc), Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD) (rcdc), Acquired Cognitive Impairment (rcdc), Machine Learning and Artificial Intelligence (rcdc), Alzheimer's Disease (rcdc), Neurosciences (rcdc), Brain Disorders (rcdc), Networking and Information Technology R&D (NITRD) (rcdc), Neurological (hrcs-hc), Aged (mesh), Aged, 80 and over (mesh), Alzheimer Disease (mesh), Apolipoproteins E (mesh), Cerebral Cortex (mesh), Cognitive Dysfunction (mesh), Demography (mesh), Discriminant Analysis (mesh), Female (mesh), Hippocampus (mesh), Humans (mesh), Longitudinal Studies (mesh), Machine Learning (mesh), Magnetic Resonance Imaging (mesh), Male (mesh), Middle Aged (mesh), Models, Neurological (mesh), Predictive Value of Tests (mesh), Prognosis (mesh), Support Vector Machine (mesh), Alzheimer's disease, classification, early diagnosis, machine learning, magnetic resonance imaging, mild cognitive impairment, predictive analytics, Alzheimer’s Disease Neuroimaging Initiative, Hippocampus (mesh), Cerebral Cortex (mesh), Humans (mesh), Alzheimer Disease (mesh), Apolipoproteins E (mesh), Magnetic Resonance Imaging (mesh), Prognosis (mesh), Discriminant Analysis (mesh), Longitudinal Studies (mesh), Predictive Value of Tests (mesh), Demography (mesh), Models, Neurological (mesh), Aged (mesh), Aged, 80 and over (mesh), Middle Aged (mesh), Female (mesh), Male (mesh), Machine Learning (mesh), Support Vector Machine (mesh), Cognitive Dysfunction (mesh), Alzheimer’s disease, classification, early diagnosis, machine learning, magnetic resonance imaging, mild cognitive impairment, predictive analytics, Aged (mesh), Aged, 80 and over (mesh), Alzheimer Disease (mesh), Apolipoproteins E (mesh), Cerebral Cortex (mesh), Cognitive Dysfunction (mesh), Demography (mesh), Discriminant Analysis (mesh), Female (mesh), Hippocampus (mesh), Humans (mesh), Longitudinal Studies (mesh), Machine Learning (mesh), Magnetic Resonance Imaging (mesh), Male (mesh), Middle Aged (mesh), Models, Neurological (mesh), Predictive Value of Tests (mesh), Prognosis (mesh), Support Vector Machine (mesh), 1103 Clinical Sciences (for), 1109 Neurosciences (for), 1702 Cognitive Sciences (for), Neurology & Neurosurgery (science-metrix), 3202 Clinical sciences (for-2020), 3209 Neurosciences (for-2020), 5202 Biological psychology (for-2020)

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/6gt8n6wz
https://escholarship.org/content/qt6gt8n6wz/qt6gt8n6wz.pdf

A note on two novel easy-to-interpret feature effect measures for partial dependence plots in a classification setting

Autoři: Rosenblad, Andreas, fil. dr, docent, 1973

Zdroj: Journal of Applied Statistics. :1-13

Témata: Black box model, effect measure, interpretable machine learning, odds ratio, relative risk, supervised learning models, Statistics, Statistik

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-566660
https://doi.org/10.1080/02664763.2025.2554823

Recent Advances in Predictive Learning Analytics: A Decade Systematic Review (2012-2022)

Autoři: Sghir, Nabila Adadi, Amina Lahmer, Mohamm

Zdroj: Education and Information Technologies. Jul 2023 28(7):8299-8333.

Peer Reviewed: Y

Page Count: 35

Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection, Models, Outcomes of Education, Data Processing, Classification, Higher Education

Optimising tunnel support design with machine learning models

Autoři: Lawal, Abiodun Ismail Zvarivadza, Tawanda Onifade, Moshood a další

Zdroj: Environmental Earth Sciences. 84(21)

Témata: Machine learning, Q-System, Tunnel support, Rock mass classification, Sensitivity analysis, Mining and Rock Engineering, Gruv- och berganläggningsteknik

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-115194
https://doi.org/10.1007/s12665-025-12573-x

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

Autoři: Ghanem, Marc Ghaith, Abdul Karim El-Hajj, Victor Gabriel a další

Zdroj: Brain Sciences. 13(12)

Témata: machine learning, artificial intelligence, deep learning, predictive modeling, spine surgery

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-520714
https://doi.org/10.3390/brainsci13121723

Enhancing coronary heart disease diagnosis: Comparative analysis of data pre-processing techniques and machine learning models using clinical medical records.

Autoři: Tseng, Chun-Wei Sun, Ling-Chun Lin, Ke-Feng a další

Zdroj: Health Informatics Journal. Jul-Sep2025, Vol. 31 Issue 3, p1-20. 20p.

Témata: *Data mining, *Medical informatics, *Electronic health records, *Machine learning, *Decision trees, Predictive tests, Random forest algorithms, Statistical models, Coronary disease, Prediction models, Research funding, Clinical decision support systems, Digital health, Descriptive statistics, Computer-aided diagnosis, Comparative studies, Data analysis software, Sensitivity & specificity (Statistics), Predictive validity

Assessment of textile sludge pyrolysis behaviour through advance predictive models for bioenergy production

Autoři: Khan, Arslan Khoja, Asif Hussain Naqvi, Salman Raza a další

Zdroj: Case Studies in Thermal Engineering. 73

Témata: Textile sludge, Pyrokinetics, Artificial neural network, Classification and regression trees, Boosted regression trees, K-nearest neighbor, Chemical Engineering, Kemiteknik

Popis souboru: electronic

Přístupová URL adresa: https://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-106421
https://doi.org/10.1016/j.csite.2025.106698

Metabolomic Profiling and Machine Learning Models for Tumor Classification in Patients with Recurrent IDH-Wild-Type Glioblastoma: A Prospective Study.

Autoři: Hodeify, Rawad Yu, Nina Balasubramaniam, Meenakshisundaram a další

Zdroj: Cancers. 16(22)

Témata: glioblastoma, machine learning, metabolomics, recurrence

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/0jd76978
https://escholarship.org/content/qt0jd76978/qt0jd76978.pdf

Protein attributes-based predictive tool in a down syndrome mouse model: a machine learning approach

Autoři: Ribeiro-Machado, Cláudia Silva, Sara Costa Aguiar, Sara a další

Přispěvatelé: Ribeiro-Machado, Cláudia Silva, Sara Costa Aguiar, Sara a další

Témata: Down syndrome, Prediction, Learning improvement, Data mining, Attributes weighting, Classification

Popis souboru: application/pdf

Dostupnost: http://hdl.handle.net/10400.22/14876

Interpretable Predictive Analytics for Online Learning: A Markov-Based Machine Learning Approach

Autoři: Chaewon Lee Lan Luo Shelbi L. Kuhlmann a další

Zdroj: Journal of Learning Analytics. 2025 12(2):259-278.

Peer Reviewed: Y

Page Count: 29

Sponsoring Agency: National Science Foundation (NSF), Division of Undergraduate Education (DUE)

Descriptors: Electronic Learning, Prediction, Data Analysis, Artificial Intelligence, Identification, At Risk Students, Learning Management Systems, College Students

Application of Radiomics-based Machine Learning models on complex cardíac diseases

Autoři: Izquierdo, Cristian

Přispěvatelé: University/Department: Universitat de Barcelona. Departament de Matemàtiques i Informàtica

Thesis Advisors: Lekadir, Karim, 1977-, Gkontra, Polyxeni, Haro, Àlex

Zdroj: TDX (Tesis Doctorals en Xarxa)

Témata: Aprenentatge automàtic, Aprendizaje automático, Machine learning, Malalties cardiovasculars, Enfermedades cardiovasculares, Cardiovascular diseases, Diagnòstic per la imatge, Diagnóstico por imagen, Diagnostic imaging, Estadística mèdica, Estadística médica, Medical statistics, Ciències Experimentals i Matemàtiques

Popis souboru: application/pdf

Přístupová URL adresa: http://hdl.handle.net/10803/692981