Fuzzy Cluster Analysis and Prediction of Psychiatric Health Data Based on BPNN-Reference-Cited by-同舟云学术

Fuzzy Cluster Analysis and Prediction of Psychiatric Health Data Based on BPNN

Published:2022-01-13 Issue: Volume:16 Page:497-503
ISSN:1998-4464
Container-title:International Journal of Circuits, Systems and Signal Processing
language:en
Short-container-title:

Author:

Xiang Hong¹,Wang Anrong¹,Fu Guoqun²,Luo Xue³,Pan Xudong¹

Affiliation:

1. School of Electronic and information Engineering, Chongqing Three Gorges University , Wanzhou, 404100, China

2. Institute of Public Administration, Chongqing Three Gorges University, Wanzhou, 404100, China

3. Students Affairs Division, Chongqing Three Gorges University, Wanzhou, 404100, China

Abstract

PMH (psychiatry/mental health) is affected by many factors, among which there are numerous connections, so the prediction of PMH is a nonlinear problem. In this paper, BPNN (Back Propagation Neural Network) is applied to fuzzy clustering analysis and prediction of PMH data, and the rules and characteristics of PMH and behavioral characteristics of people with mental disorders are analyzed, and various internal relations among psychological test data are mined, thus providing scientific basis for establishing and perfecting early prevention and intervention of mental disorders in colleges and universities. Artificial neural network is a mathematical model of information processing, which is composed of synapses similar to the structure of brain neurons. The fuzzy clustering analysis and data prediction ability of optimized PMH data are obviously improved. Applying BPNN to the fuzzy clustering analysis and prediction of PMH data, analyzing the rules and characteristics of PMH and the behavioral characteristics of patients with mental disorders, can explore various internal relations among psychological test data, and provide scientific basis for establishing early prevention and intervention of mental disorders.

Publisher

North Atlantic University Union (NAUN)

Subject

Electrical and Electronic Engineering,Signal Processing

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