An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

doi:10.1016/J.DSP.2006.09.005

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An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Journal Article•DOI•

An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease

Kemal Polat¹, Salih Güneş¹•Institutions (1)

Selçuk University¹

01 Jul 2007-Digital Signal Processing (Academic Press, Inc.)-Vol. 17, Iss: 4, pp 702-710

TL;DR: The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS using adaptive neuro-fuzzy inference system and it was very promising with regard to the other classification applications in literature for this problem.

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About: This article is published in Digital Signal Processing.The article was published on 2007-07-01. It has received 369 citations till now. The article focuses on the topics: Adaptive neuro fuzzy inference system.

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Journal Article•DOI•

Predicting Diabetes Mellitus With Machine Learning Techniques.

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Quan Zou¹, Kaiyang Qu¹, Yamei Luo, Dehui Yin, Ying Ju², Hua Tang - Show less +2 more•Institutions (2)

Tianjin University¹, Xiamen University²

06 Nov 2018-Frontiers in Genetics

TL;DR: The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used and principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) was used to reduce the dimensionality.

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Abstract: Diabetes mellitus is a chronic disease characterized by hyperglycemia. It may cause many complications. According to the growing morbidity in recent years, in 2040, the world's diabetic patients will reach 642 million, which means that one of the ten adults in the future is suffering from diabetes. There is no doubt that this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical examination data in Luzhou, China. It contains 14 attributes. In this study, five-fold cross validation was used to examine the models. In order to verity the universal applicability of the methods, we chose some methods that have the better performance to conduct independent test experiments. We randomly selected 68994 healthy people and diabetic patients' data, respectively as training set. Due to the data unbalance, we randomly extracted 5 times data. And the result is the average of these five experiments. In this study, we used principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) to reduce the dimensionality. The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used.

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468 citations

Cites methods from "An expert system approach based on ..."

...PCA (Wang and Paliwal, 2003; Polat and Günes, 2007; You et al., 2018) obtains the K vectors and unit eigenvectors by solving the characteristic equation of the correlation matrix of the observed variables....
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...Polat and Günes (2007) distinguished diabetes from normal people by using principal component analysis (PCA) and neuro fuzzy inference....
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Journal Article•DOI•

Review Article: Applications of neuro fuzzy systems: A brief review and future outline

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Samarjit Kar¹, Sujit Das², Pijush Kanti Ghosh²•Institutions (2)

National Institute of Technology, Durgapur¹, Dr. B.C. Roy Engineering College, Durgapur²

01 Feb 2014

TL;DR: The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

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Abstract: This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.

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286 citations

Cites background or methods from "An expert system approach based on ..."

...[18] K. Polat, K. Gunes, An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease, Digital Signal Processing 17 (4) (2007) 702–710....
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...Authors/years Methodology Domain Article type Neagoe et al. [12] (2003) Fuzzy-Gaussion neural network, Ischemic heart disease Experimental Mastorocostas and Hilas [13] (2004) FNN, least square methods Lung sound analysis Analytical Mastorocostas & Theocharis [14] (2005) Recurrent filter, fuzzy neural network Lung sounds separation Experimental Guler et al. [15] (2005) Wavelet transform, ANFIS Brain disorder Classification Oweis et al. [16] (2005) NF approach Bio-medical Classification Subasi [30] (2006) Neuro-fuzzy logic technique, discrete Epileptic seizure Analytical Wavelet transform Stavrakoudis et al. [17] (2007) Fuzzy neural filter, TSK fuzzy network Lung soung separation Experimental Polat & Gunes [18] (2007) PCA, ANFIS, expert system Diabetes diagnosis Diagnosis Sengur [19] (2008) ANFIS, LDA Heart disease diagnosis Comparative Ubeyli [20] (2009) ANFIS, decision making, Lyapunov-exponent ECG signal classification Classification Ovreiu and Simon [21] (2010) EA, BPO, NF Rules Cardiac disease Simulation Alamelumangai and DeviShree [22] (2010) Neuro fuzzy model, PSO Breast cancer diagnosis Optimization Obi and Imainvan [23] (2011) NFI procedure Alzheimer Diagnosis Obi and Imianvan [24] (2011) NFI procedure Leukemia Diagnosis Kumar et al. [25] (2011) Fast ANFIS, LM Algorithm Cancer diagnosis Experimental Agboizebeta and Chukwuyeni [26] (2012) Neuro-fuzzy inference (NFI) system Thyroid detection Demonstrating d c r i A n s d o ( d p m s s d y n p t t a p 2 d o t c f d h o Agboizebeta and Chukwuyeni [27] (2012) NFI procedure Ephzibah and Sundarapandian [28] (2012) GA, ANN Khameneh et al. [29] (2012) ANFIS iagnostic accuracy of diabetes disease....
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...Polat & Gunes [18] (2007) PCA, ANFIS, expert system Diabetes diagnosis Diagnosis...
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...In the same year (2007), Polat and Gunes [18] paid their attention for diabetes patients....
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Journal Article•DOI•

A comparative study on diabetes disease diagnosis using neural networks

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Hasan Temurtaş, Nejat Yumusak¹, Feyzullah Temurtas•Institutions (1)

Sakarya University¹

01 May 2009-Expert Systems With Applications

TL;DR: A comparative pima-diabetes disease diagnosis was realized through proper interpretation of the diabetes data using a multilayer neural network structure trained by Levenberg-Marquardt (LM) algorithm and a probabilistic neuralnetwork structure used.

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Abstract: Diabetes occurs when a body is unable to produce or respond properly to insulin which is needed to regulate glucose. Besides contributing to heart disease, diabetes also increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. In this study, a comparative pima-diabetes disease diagnosis was realized. For this purpose, a multilayer neural network structure which was trained by Levenberg-Marquardt (LM) algorithm and a probabilistic neural network structure were used. The results of the study were compared with the results of the pervious studies reported focusing on diabetes disease diagnosis and using the same UCI machine learning database.

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280 citations

Cites background or methods or result from "An expert system approach based on ..."

...…this manuscript, Pima Indian diabetes dataset taken from the UCI machine learning respiratory were used (Carpenter & Markuzon, 1998; Deng & Kasabov, 2001; Kayaer & Yıldırım, 2003; Polat & Gunes, 2007; Polat et al., 2008; ftp://ftp.ics.uci.edu/pub/machine-learningdatabases (accessed: 15.02.2008))....
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...This result is very close to the result (89.47% classification accuracy) obtained by Polat and Gunes(2007)....
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...In Type 2 diabetes, either the body does not produce enough insulin or the cells ignore the insulin (Polat & Gunes, 2007)....
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...For the 10-fold cross-validation method, the classification accuracy of MLNN with LM obtained by this study using correct training was a bit better than those obtained by other studies except the classification accuracies by Polat and Gunes (2007) which is not reproducible....
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...For the 10-fold cross-validation method, the classification accuracy of MLNN with LM obtained by this study using correct training was a bit better than those obtained by other studies except the classification accuracies by Polat and Gunes (2007) which is not reproducible as seen in Table 1....
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Journal Article•DOI•

A Fuzzy Expert System for Diabetes Decision Support Application

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Chang-Shing Lee¹, Mei-Hui Wang¹•Institutions (1)

National University of Tainan¹

01 Feb 2011

TL;DR: A novel fuzzy expert system can work effectively for diabetes decision support application and the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application.

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Abstract: An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application.

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243 citations

Cites result from "An expert system approach based on ..."

...The proposed approach can analyze the personal physical data of the PIDD and generate corresponding human knowledge based on the FDO....
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...The experimental PIDD is retrieved from the Internet (http://archive.ics.uci.edu/ml/) and it contains the collected personal data of the Pima Indian population....
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...The final experiment compares the accuracy of the proposed method with results of studies involving the PIDD [4], [5], [8]....
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...The experimental environment was constructed to evaluate the performance of the proposed approach; in addition, PIDD was chosen as the evaluated data set....
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...For the PIDD, each case has nine attributes, listed in Table I, and each attribute can be constructed as a fuzzy variable with some fuzzy numbers....
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Journal Article•DOI•

Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System

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Gunasekaran Manogaran¹, R. Varatharajan, M. K. Priyan¹•Institutions (1)

VIT University¹

01 Feb 2018-Multimedia Tools and Applications

TL;DR: The proposed MKL with ANFIS based deep learning method follows two-fold approach and has produced high sensitivity, high specificity and less Mean Square Error for the for the KEGG Metabolic Reaction Network dataset.

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Abstract: Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System (MKL with ANFIS) based deep learning method is proposed in this paper for heart disease diagnosis. The proposed MKL with ANFIS based deep learning method follows two-fold approach. MKL method is used to divide parameters between heart disease patients and normal individuals. The result obtained from the MKL method is given to the ANFIS classifier to classify the heart disease and healthy patients. Sensitivity, Specificity and Mean Square Error (MSE) are calculated to evaluate the proposed MKL with ANFIS method. The proposed MKL with ANFIS is also compared with various existing deep learning methods such as Least Square with Support Vector Machine (LS with SVM), General Discriminant Analysis and Least Square Support Vector Machine (GDA with LS-SVM), Principal Component Analysis with Adaptive Neuro-Fuzzy Inference System (PCA with ANFIS) and Latent Dirichlet Allocation with Adaptive Neuro-Fuzzy Inference System (LDA with ANFIS). The results from the proposed MKL with ANFIS method has produced high sensitivity (98%), high specificity (99%) and less Mean Square Error (0.01) for the for the KEGG Metabolic Reaction Network dataset.

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195 citations

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Abstract: Introduction This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvec-tors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar. There are examples all the way through this tutorial that are meant to illustrate the concepts being discussed. If further information is required, the mathematics textbook " Elementary Linear Algebra 5e " ISBN 0-471-85223-6 is a good source of information regarding the mathematical background .

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Abstract: From the Publisher: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets. The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called "discovery in databases." This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search. Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in. Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTELaboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project.

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University of California, Berkeley¹

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Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients

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İnan Güler¹, Elif Derya Übeyli²•Institutions (2)

Gazi University¹, TOBB University of Economics and Technology²

30 Oct 2005-Journal of Neuroscience Methods

TL;DR: The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANfIS model has potential in classifying the EEG signals.

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