Architecture of Back Propagation Neural Network Model for Early Detection of Tendency to Type B Personality Disorders

Cynthia Hayat,Noviyanti Sagala,Samuel Limong

doi:10.23917/khif.v5i2.7923

Abstract

Personal disorder is a type of mental illness. People with personal disorder can not respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detetction of this personal disorder, so that it can be anticipated properly. This paper reports an architecture model of back propagation neural network (BPPN) for early detection of type B personal disorder. The back propagation process divided into two phases, i.e training and testing. The training process used 43 data and the testing process used 34 data. The output classified into 4 diagnosis category of type B personal disorder, I.e. anti social, borderline, histrionic, and narcissistics. The optimal parameters of BPPN model consist of maximum epoch of 1000, maximum mu of 10000000000, increase mu of 25, decrease mu of 0.1, and neuron hidden layer of 25. The MSE of training is 3.07E-14 and MSE of testing is 1.00E-03. The accuracy of training is 90.7%, while the accuracy of testing is 97.2%.

Highlights

Mental Health of America defines personality disorder as a setback that occurs in the internal and external of the human self where the person tends to be inflexible, rigid and unable to respond to changes and the demands of life [1]
Artificial Neural Network (ANN) is a concept of artificial knowledge in the field of artificial intelligence that made by adopting the human nervous system in the brain
The study conducted by Panpan Hu stated that artificial neural networks can be used to analyze patterns of psychological patients, where the ANN model developed has a high accuracy training level with an average of 98.2%

Summary

Introduction

Mental Health of America defines personality disorder as a setback that occurs in the internal and external of the human self where the person tends to be inflexible, rigid and unable to respond to changes and the demands of life [1]. With the application of artificial neural networks, it is expected to be able to construct a modeling to initiate early diagnosis of the type B personality disorder tendency. The study conducted by Panpan Hu stated that artificial neural networks can be used to analyze patterns of psychological patients, where the ANN model developed has a high accuracy training level with an average of 98.2%. ANN has a good training ability with a sufficient prediction level and the method can support in determining the diagnosis based on the results of the analysis [6]. Elvia Budianita and Muhammad Firdaus reported the method of artificial neural networks use Learning Vector Quantization 2 (LVQ2) in diagnosing psychiatric illnesses in which has a good accuracy of 90% [7]. The results of the expert system are compared with the results of the experts with a percentage of accuracy of 83.01% [8]

Methods

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Discussion

Conclusion