Cargando…

An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors

This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known...

Descripción completa

Detalles Bibliográficos
Autores principales:	Sun, Cheuk-Kay, Tang, Yun-Xuan, Liu, Tzu-Chi, Lu, Chi-Jie
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9368335/ https://www.ncbi.nlm.nih.gov/pubmed/35955112 http://dx.doi.org/10.3390/ijerph19159756

Ejemplares similares

Application of SHAP for Explainable Machine Learning on Age-Based Subgrouping Mammography Questionnaire Data for Positive Mammography Prediction and Risk Factor Identification
por: Sun, Jeffrey, et al.
Publicado: (2023)

Predictors of mammographic microcalcifications
por: Azam, Shadi, et al.
Publicado: (2020)

An integrated machine learning predictive scheme for longitudinal laboratory data to evaluate the factors determining renal function changes in patients with different chronic kidney disease stages
por: Tsai, Ming-Hsien, et al.
Publicado: (2023)

Interpretation of mammographic breast microcalcification: interobserver variability between radiologists and mammographers and analysis of best mammographic predictors of histopathological outcomes
por: Naidu, LS, et al.
Publicado: (2006)

A Comprehensive Study of Anomaly Detection Schemes in IoT Networks Using Machine Learning Algorithms
por: Diro, Abebe, et al.
Publicado: (2021)

Mammographic screening and mammographic patterns
por: Day, Nick, et al.
Publicado: (2000)

Mammographic density. Measurement of mammographic density
por: Yaffe, Martin J
Publicado: (2008)

Important Risk Factors in Patients with Nonvalvular Atrial Fibrillation Taking Dabigatran Using Integrated Machine Learning Scheme—A Post Hoc Analysis
por: Huang, Yung-Chuan, et al.
Publicado: (2022)

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine
por: Zamry, Nurfazrina M., et al.
Publicado: (2021)

Adapting the pre-trained convolutional neural networks to improve the anomaly detection and classification in mammographic images
por: Saber, Abeer, et al.
Publicado: (2023)

Pre-earthquake anomaly extraction from borehole strain data based on machine learning
por: Chi, Chengquan, et al.
Publicado: (2023)

Mammographic density as a predictor of breast cancer survival: the Multiethnic Cohort
por: Maskarinec, Gertraud, et al.
Publicado: (2013)

Hyperthyroidism is associated with breast cancer risk and mammographic and genetic risk predictors
por: Yang, Haomin, et al.
Publicado: (2020)

Mammographic density
por: Boyd, Norman F, et al.
Publicado: (2009)

Developing a Novel Machine Learning-Based Classification Scheme for Predicting SPCs in Breast Cancer Survivors
por: Chang, Chi-Chang, et al.
Publicado: (2019)

Identifying Predictors of COVID-19 Mortality Using Machine Learning
por: Wan, Tsz-Kin, et al.
Publicado: (2022)

A proficiency assessment of integrating machine learning (ML) schemes on Lahore water ensemble
por: Shahid, Nazish
Publicado: (2023)

Integrating a Hive Triangle Pattern with Subpixel Analysis for Noncontact Measurement of Structural Dynamic Response by Using a Novel Image Processing Scheme
por: Lu, Yung-Chi, et al.
Publicado: (2014)

Topological quantization of physical parameters, global anomalies and the stochastic scheme
por: Nissimov, Emil R, et al.
Publicado: (1986)

Mammographic image classification with deep fusion learning
por: Yu, Xiangchun, et al.
Publicado: (2020)

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk
por: Hopper, John L, et al.
Publicado: (2020)

Assessment of the digital competencies of university instructors through use of the machine learning method
por: Yang, Tzu-Chi
Publicado: (2023)

Predictors of mammographic density among women with a strong family history of breast cancer
por: Moran, Olivia, et al.
Publicado: (2019)

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection
por: Malek, Aminah Abdul, et al.
Publicado: (2023)

Network anomaly detection: a machine learning perspective
por: Bhattacharyya, Dhruba Kumar
Publicado: (2013)

The association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic Density
por: Ward, Sarah V., et al.
Publicado: (2022)

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme
por: Biglari, Amin, et al.
Publicado: (2023)

Machine learning strategy for identifying altered gut microbiomes for diagnostic screening in myasthenia gravis
por: Chang, Che-Cheng, et al.
Publicado: (2023)

Genetic determinants of mammographic density
por: Haiman, Christopher A, et al.
Publicado: (2002)

Computer-assisted mammographic imaging
por: Boggis, Caroline RM, et al.
Publicado: (2000)

Breast Hamartoma: Mammographic Findings
por: Farrokh, Donya, et al.
Publicado: (2011)

Determinants of Mammographic Density Change
por: Azam, Shadi, et al.
Publicado: (2019)

Mammographic compression in Asian women
por: Lau, Susie, et al.
Publicado: (2017)

Efficient Lattice-Based Ring Signature Scheme without Trapdoors for Machine Learning
por: Ye, Qing, et al.
Publicado: (2022)

Digital Breast Tomosynthesis Versus Additional Diagnostic Mammographic Views for the Evaluation of Asymmetric Mammographic Densities
por: Waheed, Hira, et al.
Publicado: (2020)

Influence of mammographic density and compressed breast thickness on true mammographic sensitivity: a cohort study
por: Strandberg, Rickard, et al.
Publicado: (2023)

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques
por: Jhou, Mao-Jhen, et al.
Publicado: (2022)

Contrast-Enhanced Mammographic Features of In Situ and Invasive Ductal Carcinoma Manifesting Microcalcifications Only: Help to Predict Underestimation?
por: Cheung, Yun-Chung, et al.
Publicado: (2021)

Analysis of a machine learning–based risk stratification scheme for acute kidney injury in vancomycin
por: Mu, Fei, et al.
Publicado: (2022)

Practical machine learning : a new look at anomaly detection
por: Dunning, Ted, et al.
Publicado: (2014)

Cannot write session to /tmp/vufind_sessions/sess_ageqtcn2n8vb1lcjfhj5rnfied