A hybrid intelligent system for medical data classification

Seera, Manjeevan; Lim, Chee Peng

doi:10.1016/j.eswa.2013.09.022

Cited by 261 publications

(90 citation statements)

References 68 publications

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“…The results compared the proposed system i.e. GA_RBF NN, RBF NN with the previous results reported by earlier methods (Ganji and Abadeh, 2011;Seera and Lim, 2014). As per Table 2, it can seen that by applying the GA approach, 4 attributes have been obtained among 8 attributes.…”

Section: Phase Ii: Testing Processmentioning

confidence: 54%

“…The same dataset has been used in the reference (Kayaer and Yildirim, 2003;Goncalves et al, 2006;Polat and Gunes, 2007;Kahramanli and Allahverdi, 2008;Temurtas et al, 2009;Dogantekin et al, 2010;Ganji and Abadeh, 2010;Jayalakshmi and Santhakumaran, 2010;Ephzibah, 2011;Ganji and Abadeh, 2011;Kala et al, 2011;Karegowda et al, 2011;Lee, 2011;Lukka, 2011;Orkcu and Bal, 2011;Qasem and Shamsuddin, 2011;Selvakuberan et al, 2011;Karatsiolis and Schizas, 2012;Aslam et al, 2013;Das et al, 2013;Kalaiselvi and Nasira, 2014;Seera and Lim, 2014;Choubey and Paul, 2016 …”

Section: Used Diabetes Disease Datasetmentioning

confidence: 99%

“…In this PCA is used to reduce the dimensions of diabetes disease datasets features and ANFIS diagnosis of diabetes disease means applying classification of the reduced features of diabetes disease datasets. Seera and Lim (2014) introduced Fuzzy Min-Max neural network, Classification and Regression Tree (CART), Random Forest (RF) for the classification of medical data using hybrid intelligence system. The methodology is implemented on various datasets including Breast Cancer Wisconsin, PIDD, and Liver Disorders and performs better as compared to other existing techniques.…”

Section: Related Workmentioning

confidence: 99%

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GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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Abstract:The modern society is prone to many life-threatening diseases, which if diagnosed early, can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes disease. There are already several existing methods, which have been implemented for the diagnosis of diabetes dataset. Here, the proposed approach consists of two stages: in first stage Genetic algorithm (GA) used as an attribute (feature) selection which reduces 4 attributes among 8 attributes, and in the second stage Radial Basis Function Neural Network (RBF NN) has been used for classification on selected attributes among all the attributes. The experimental results show the performance of the proposed methodology on Pima Indian Diabetes Dataset (PIDD) and provide better classification for diagnosis of diabetes patients on PIDD. GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can also be used for other kinds of medical diseases.

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Section: Phase Ii: Testing Processmentioning

confidence: 54%

Section: Used Diabetes Disease Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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GA_RBF NN: a classification system for diabetes

Choubey

Paul

2017

IJBET

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“…Medical data classification tasks are executed using different varieties of data types including text, signal, image, DNA, voice, etc. [1][2][3][4][5][6][7][8][9][10]. Some of the available literature [1][2][3][4][5][6] focus on medical data classification tasks for ailments such as diabetes, heart disease, hepatitis, Parkinson, liver, and cancer.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10]. Some of the available literature [1][2][3][4][5][6] focus on medical data classification tasks for ailments such as diabetes, heart disease, hepatitis, Parkinson, liver, and cancer. Similarly, EEG and ECG signals are usually used in diagnosing other diseases such as epileptic seizure, schizophrenia, Alzheimer, asthma, and arrhythmia [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis

Iliyasu

Fatichah

2016

Automatika

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Original scientific paperTraditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the 'standard' Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate properties of the Possibilistic C-Means (PCM) and Fuzzy C-Means (FCM) algorithms into a sophisticated clustering algorithm. Notwithstanding the tremendous capabilities offered by this hybrid technique, in terms of structure, it resembles the hEAC and fEAC ensemble clustering techniques that are realised by integrating the K-Means and FCM clustering algorithms into the EAC technique. To validate the new technique's effectiveness, its performance on both synthetic and real medical datasets was evaluated alongside individual runs of well-known clustering methods, other unsupervised ensemble clustering techniques and some supervised machine learning methods. Our results show that the proposed pEAC technique outperformed the individual runs of the clustering methods and other unsupervised ensemble techniques in terms accuracy for the diagnosis of hepatitis, cardiovascular, breast cancer, and diabetes ailments that were used in the experiments. Remarkably, compared alongside selected supervised machine learning classification models, our proposed pEAC ensemble technique exhibits better diagnosing accuracy for the two breast cancer datasets that were used, which suggests that even at the cost of none labelling of data, the proposed technique offers efficient medical data classification.Key words: Evidence accumulation clustering, K-means, fuzzy C-means, possibilitic fuzzy C-means, hybrid intelligent systems, health informatics, medical data classification, disease diagnosis.Grupiranje zasnovano na skupljanju dokaza s vjerojatnosno-neizrazitim C-means pristupom za dijagnozu bolesti. Tradicionalno, metode nadziranog strojnog učenja predstavljaju prvi izbor za zadatke koji uključuju klasifikaciju podataka. Ovo istraživanje prikazuje nekonvencionalnu hibridnu alternativnu (pEAC) tehniku koja kombinira vjerojatnosno-neizraziti C-Means (PFCM) kao osnovni algoritam grupiranja u standardno grupiranje korištenjem grupiranja zasnovanog na skupljanju dokaza (EAC). PFCM objedinjuje zasebna svojstva vjerojatnosnog C-Means (PCM) i neizrazitog C-Means (FCM) algoritama u sofisticirani algoritam grupiranja. Usprkos ogromnim mogućnostima koje nudi ova tehnika, u smislu strukture, ona je nalik cjelovitim hEAC i fEAC tehnikama grupiranja realiziranim integracijom K-Means i FCM algoritama grupiranja u EAC tehniku. Kako bi se validirala učinkovi-tost, njeno ponašanje je ispitano na sintetičkim i stvarnim medicinskim podacima te su provedene usporedbe s pojedinačnim široko rasprostranjenim metodama, drugim nenadziranim tehnikama grupiranja i nekim nadziranim metodama učenja. Rezultat prikazuje kako predložena pEAC tehnika nadmašuje poje...

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