Bayesian Classifier Based Validation Method for Multivariate Systems

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

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Influence of feature weight on text categorization performance of Bayesian classifier

Journal of Computer Applications ◽

10.3724/sp.j.1087.2008.03080 ◽

2009 ◽

Vol 28 (12) ◽

pp. 3080-3083 ◽

Cited By ~ 1

Author(s):

Xiu-mei GAO ◽

Fang CHEN ◽

Feng-xi SONG ◽

Zhong JIN

Keyword(s):

Text Categorization ◽

Bayesian Classifier ◽

Feature Weight

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Evaluation of Bayesian Classifier for Salt Dome Detection using Texture analysis

2nd Smart Cities Symposium (SCS 2019) ◽

10.1049/cp.2019.0187 ◽

2019 ◽

Author(s):

G.M. BinMakhashen ◽

H. Luqman ◽

M. Ahmed

Keyword(s):

Texture Analysis ◽

Salt Dome ◽

Bayesian Classifier

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Validation method for body-mounted sensor attachments

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-2006 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Katharina Schmidt ◽

David Hochmann

Keyword(s):

Evaluation Method ◽

Tracking System ◽

The Body ◽

Exploratory Research ◽

Body Segment ◽

Validation Method ◽

Sensor Position ◽

Validation Parameters ◽

Measurement Units ◽

Soft Tissue Artifact

AbstractSmall sensor devices like inertial measurement units enable mobile movement and gait analysis, whereby existing systems differ in data acquisition, data processing, and gait parameter calculation. Concerning the validation, recent studies focus on the captured motion and the influence of sensor positioning with respect to the accuracy of the computed biomechanical parameters in comparison to a reference system. Although soft tissue artifact is a major source of error for skin-mounted sensors, there are no investigations regarding the relative movement between the body segment and sensor attachment itself. The aim of this study is to find an evaluation method and to determine parameters that allow the validation of various sensor attachment types and different sensor positionings. The analysis includes the comparison between an adhesive and strap attachment variant as well as the frontal and lateral sensor placement. To validate different attachments, an optical marker-based tracking system was used to measure the body segment and sensor position during movement. The distance between these two positions was calculated and analyzed to determine suitable validation parameters. Despite the exploratory research, the results suggest a feasible validation method to detect differences between the attachments, independent of the sensor type. To have representative and statistically validated results, further studies that involve more participants are necessary.

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Sparseness of the LS-SVM algorithm based on the leave one out cross validation method with Multibeam data

Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer Engineering ◽

10.1145/3443467.3443763 ◽

2020 ◽

Author(s):

Bo Zhang ◽

Xianyuan Huang ◽

Long Fan ◽

Guobin Chang

Keyword(s):

Cross Validation ◽

Validation Method ◽

Svm Algorithm ◽

Leave One Out ◽

Multibeam Data

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Effectiveness of the Validation Method in Work Satisfaction and Motivation of Nursing Home Care Professionals: A Literature Review

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010201 ◽

2020 ◽

Vol 18 (1) ◽

pp. 201

Author(s):

Iván Sánchez-Martínez ◽

Raül Vilar ◽

Javier Irujo ◽

Duna Ulsamer ◽

Dolors Cano ◽

...

Keyword(s):

Job Satisfaction ◽

Literature Review ◽

Older People ◽

Single Case ◽

Case Series ◽

Nursing Home Care ◽

Validation Method ◽

People With Dementia ◽

Cochrane Database ◽

Quasi Experimental

The purpose of this study was to carry out a literature review on the effectiveness of the validation method (VM) in job satisfaction and motivation of care professionals working with older people in nursing homes. The review was carried out in specialised databases: Scopus, PsychINFO, PubMed, Web of Science (WOS), Google Scholar, Scielo, and Cochrane Database of Systematic Reviews. 9046 results were obtained, out of which a total of 14 studies met the inclusion criteria: five quantitative, four qualitative, one single case series, two quasi-experimental and two mixed methods studies. The results of the analysed studies report that the VM can be an effective tool that facilitates communication and interaction in care, reducing levels of stress and job dissatisfaction among care professionals. The VM facilitates communication between professionals and older people with dementia, and improves the management of complex situations that may arise in care, directly influencing a reduction in work stress and increasing job satisfaction.

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