Two-Stage Neural Network Classifier for the Data Imbalance Problem with Application to Hotspot Detection

Medical data classification analysis the medical data of the patients to predict the diseases risk. Data mining techniques were highly used in the medical data classification and predicted the diseases. Many existing methods were use the various classifier and feature selection to improve the performance of the classification. Although data imbalance problem is need to be solved for increases the performance. In this research, Synthetic Minority Over-sampling TEchnique (SMOTE) techniques is used for solving the data imbalance problem and Recurrent Neural Network (RNN) was used for the classification. The SMOTE method based on the k Nearest Neighbor (kNN) for the over-sample and under-sample the attributes. The RNN process the instance independent of the previous instance for the classification. Four medical datasets of University of California, Irvine (UCI) were used to evaluate the effectiveness of the proposed SMOTE-RNN method. The proposed SMOTE-RNN method has the accuracy of 85 % while existing method has 82 % accuracy.

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Mechanical fault diagnosis using wireless sensor networks and a two-stage neural network classifier

2009 IEEE Aerospace conference ◽

10.1109/aero.2009.4839671 ◽

2009 ◽

Cited By ~ 9

Author(s):

P. Ballal ◽

A. Ramani ◽

M. Middleton ◽

C. McMurrough ◽

A. Athamneh ◽

...

Keyword(s):

Neural Network ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

Fault Diagnosis ◽

Wireless Sensor ◽

Neural Network Classifier ◽

Two Stage

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Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

Methods of Information in Medicine ◽

10.1055/s-0038-1636858 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 349-351

Author(s):

H. Mizuta ◽

K. Kawachi ◽

H. Yoshida ◽

K. Iida ◽

Y. Okubo ◽

...

Keyword(s):

Neural Network ◽

Decision Support ◽

Psychiatric Diagnosis ◽

Psychiatric Patients ◽

Bayesian Classifier ◽

Neural Network Classifier ◽

Correct Decision ◽

Neurotic Disorders ◽

The Neural Network

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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A novel parallel neural network classifier for automatic vehicle license plate recognition

Artificial Intelligence and Industrial Application ◽

10.2495/aiia140151 ◽

2015 ◽

Author(s):

X. Han ◽

H. M. Xie ◽

G. Q. Zhang

Keyword(s):

Neural Network ◽

License Plate ◽

License Plate Recognition ◽

Neural Network Classifier ◽

Vehicle License Plate Recognition

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Withdrawn: Automated Diagnosis of Glaucoma Using Deep Neural Network Classifier

Current Signal Transduction Therapy ◽

10.2174/1574362414666190906112910 ◽

2019 ◽

Vol 14 ◽

Author(s):

M. Madhumalini ◽

T. Meera Devi

Keyword(s):

Neural Network ◽

Signal Transduction ◽

Deep Neural Network ◽

Neural Network Classifier ◽

Current Signal ◽

Automated Diagnosis ◽

Signal Transduction Therapy ◽

Copyright Permission ◽

Legal Right

The article has been withdrawn on the request of the authors and the editor of the journal Current Signal Transduction Therapy. Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers, if and when the article is accepted for publication.

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