A Commonly Occurring Incomplete Multiple Classification Model

Biometrics ◽  
1963 ◽  
Vol 19 (1) ◽  
pp. 105
Author(s):  
Klaus Hinkelmann
Keyword(s):  
Classification Model ◽  
Multiple Classification

scholarly journals A Multilevel Analysis of Neighbourhood, School, Friend and Individual-Level Variation in Primary School Children’s Physical Activity

2019 ◽  
Vol 16 (24) ◽  
pp. 4889
Author(s):  
Ruth Salway ◽  
Lydia Emm-Collison ◽  
Simon J. Sebire ◽  
Janice L. Thompson ◽  
Deborah A. Lawlor ◽  
...  
Keyword(s):  
Physical Activity ◽  
Vigorous Physical Activity ◽  
Classification Model ◽  
Accelerometer Data ◽  
Individual Level ◽  
Friendship Groups ◽  
Multiple Classification ◽  
Multiple Membership ◽  
Children’S Physical Activity ◽  
Different Levels

Physical activity is influenced by individual, inter-personal and environmental factors. In this paper, we explore the variability in children’s moderate-to-vigorous physical activity (MVPA) at different individual, parent, friend, school and neighbourhood levels. Valid accelerometer data were collected for 1077 children aged 9, and 1129 at age 11, and the average minutes of MVPA were derived for weekdays and weekends. We used a multiple-membership, multiple-classification model (MMMC) multilevel model to compare the variation in physical activity outcomes at each of the different levels. There were differences in the proportion of variance attributable to the different levels between genders, for weekdays and weekends, at ages 9 and 11. The largest proportion of variability in MVPA was attributable to individual variation, accounting for half of the total residual variability for boys, and two thirds of the variability for girls. MVPA clustered within friendship groups, with friends influencing peer MVPA. Including covariates at the different levels explained only small amounts (3%–13%) of variability. There is a need to enhance our understanding of individual level influences on children’s physical activity.

scholarly journals Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature (Preprint)

2017 ◽  
Author(s):  
Jette Henderson ◽  
Junyuan Ke ◽  
Joyce C Ho ◽  
Joydeep Ghosh ◽  
Byron C Wallace
Keyword(s):  
Patient Characteristics ◽  
Classification Model ◽  
Journal Articles ◽  
Evaluation Tool ◽  
Web Based ◽  
Nosql Databases ◽  
Multiple Classification ◽  
Essential Properties ◽  
Order Of Magnitude

BACKGROUND Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making. OBJECTIVE The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved. METHODS PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET’s phenotype representation with PheKnow-Cloud’s by using PheKnow-Cloud’s experimental setup. In PIVET’s framework, we also introduce a statistical model trained on domain expert–verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner. RESULTS PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET’s analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes. CONCLUSIONS Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.

Research of Web-Page Multi-Class Classification System Based on Support Vector Machine

2015 ◽  
Vol 713-715 ◽  
pp. 2312-2316
Author(s):  
Feng Chen ◽  
Rong Chen ◽  
Gen Cheng Wang
Keyword(s):  
Support Vector Machine ◽  
Classification Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Web Page ◽  
Web Documents ◽  
Web Page Classification ◽  
Multiple Classification ◽  
Multi Class Classification ◽  
Page Classification

A Chinese web-page classification algorithm based on SVM including the important aspects of text preprocessing, feature selection and multiple-Classification algorithm. In this paper, based on the analyses of features of Web documents, this paper does research the approach of classification in Support Vector Machine (SVM) and select of Kernel function. Furthermore, a web-page classification model and algorithm that is based on Binary Tree SVM is presented. The experiments show that it not only reduces the size of train set, but also has very high training efficiency. Its precision and recall are better.

scholarly journals Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5982
Author(s):  
Dat Tien Nguyen ◽  
Min Beom Lee ◽  
Tuyen Danh Pham ◽  
Ganbayar Batchuluun ◽  
Muhammad Arsalan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Performance ◽  
Classification Model ◽  
Site Classification ◽  
Medical Systems ◽  
Cad System ◽  
Learning Techniques ◽  
Multiple Classification ◽  
Processing Techniques

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.

A glance on dystonias, how to recognize and handle them

2019 ◽  
pp. 22-29
Author(s):  
F. N. Mercan ◽  
E. Bayram ◽  
M. C. Akbostanci
Keyword(s):  
Differential Diagnosis ◽  
Movement Disorder ◽  
Age Of Onset ◽  
Clinical Manifestations ◽  
Body Part ◽  
Classification Model ◽  
Treatment Choices ◽  
Muscle Groups

Dystonia refers to an involuntary, repetitive, sustained, painful and twisting movements of the affected body part. This movement disorder was first described in 1911 by Hermain Oppenheim, and many studies have been conducted to understand the mechanism, the diagnosis and the treatment of dystonia ever since. However, there are still many unexplained aspects of this phenomenon. Dystonia is diagnosed by clinical manifestations, and various classifications are recommended for the diagnosis and the treatment. Anatomic classification, which is based on the muscle groups involved, is the most helpful classification model to plan the course of the treatment. Dystonias can also be classified based on the age of onset and the cause. These dystonic syndromes can be present without an identified etiology or they can be clinical manifestations of a neurodegenerative or neurometabolic disease. In this review we summarized the differential diagnosis, definition, classifications, possible mechanisms and treatment choices of dystonia.

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