Alzheimer\'s Disease Classification Using Hybrid Neuro Fuzzy Runge-Kutta (HNFRK) Classifier

Sampath, R.; Saradha, A.

doi:10.19026/rjaset.10.2550

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…A significant amount of research has been conducted on mental health with intentions to automate the diagnostic processes using AI [17,18]. The technique proposed in this paper is the first of its kind used for diagnosing postpartum depression disorder.…”

Section: Discussionmentioning

confidence: 99%

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A neuro fuzzy approach for the diagnosis of postpartum depression disorder

Osubor

Egwali

2018

Iran J Comput Sci

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Postpartum depression is a growing public health problem amongst nursing mothers, which is not given much attention in primary health care settings. It is a type of depression experienced after childbirth that affects an estimated 13-19% of nursing mothers. Postpartum depression is very difficult to diagnose and by concentrating on somatic illnesses, most medical practitioners frequently fail to recognize it. In this paper an Adaptive Neuro Fuzzy Inference System was utilized to predict postpartum depression. Thirty-six data instances were used in training the model. The system had a training error of 7.0706e−005 at epoch 1 and an average testing error of 3.0185. This technique will facilitate the prompt and accurate diagnosis of postpartum depression.

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Section: Discussionmentioning

confidence: 99%

“…Literatures have shown that ANFIS-based intelligence systems for diagnosing medical illnesses have yielded excellent results for some mental health-related conditions [17,18]. ANFIS combines both neural network and fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

A neuro fuzzy approach for the diagnosis of postpartum depression disorder

Osubor

Egwali

2018

Iran J Comput Sci

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“…for AD have been taken as input India, vinodsharma@jammuuniversity.in parameters each with three input linguistic values (low, medium and high) and one output. R. Sampath et al[6] implemented a hybrid Neuro-Fuzzy Runge Kutta (HNFRK) Classifier to predict Alzheimer disease from the data set of 150 fMRI images consisting of 95 AD and 55 healthy controls samples. The proposed approach started with preprocessing the fMRI images using histogram based thresholding approach and then normalization of preprocessed image to MNI standard using SPM2 has been performed.…”

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confidence: 99%

Brief Paper: Evaluation of Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means based ANFIS System in Diagnosis of Alzheimer

Kour¹,

Manhas²,

Sharma³

2019

J Multimed Inf Syst

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Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

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