Hybrid Metaheuristics for Medical Data Classification

AlMuhaideb, Sarab; Menaï, Mohamed El Bachir

doi:10.1007/978-3-642-30671-6_7

Cited by 15 publications

(9 citation statements)

References 72 publications

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“…Different metrics of medical data classification and how it has been adapted for diagnosis of disease and prediction of any disease based on different models is presented in [2]. The application of dispersed medical data in decision making is discussed in [3].The author used both local data and global data with collision.…”

Section: Related Workmentioning

confidence: 99%

CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

Ananthajothi¹,

Subramaniam²

2019

IJITEE

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The problem of medical data classification is analyzed and the methods of classification are reviewed in various aspects. However, the efficiency of classification algorithms is still under question. With the motivation to leverage the classification performance, a Class Level disease Convergence and Divergence (CLDC) measure based algorithm is presented in this paper. For any dimension of medical data, it convergence or divergence indicates the support for the disease class. Initially, the data set has been preprocessed to remove the noisy data points. Further, the method estimates disease convergence/divergence measure on different dimensions. The convergence measure is computed based on the frequency of dimensional match where the divergence is estimated based on the dimensional match of other classes. Based on the measures a disease support factor is estimated. The value of disease support has been used to classify the data point and improves the classification performance.

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Section: Related Workmentioning

confidence: 99%

CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

Ananthajothi¹,

Subramaniam²

2019

IJITEE

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“…Data for diagnostic procedures are usually data types including text, signals, images, voice, etc. [1][2][3][4][5][6][7][8][9][10][11] that are sourced from a variety of sources within the health care records, such as the doctor's notes, the laboratory results, radiological results, pathological results, and many other sources. These health records of patients are collected into a dataset that is used to diagnose new patients based on the dataset.…”

Section: Introductionmentioning

confidence: 99%

A Combined AdaBoost and NEWFM Technique for Medical Data Classification

Iliyasu

Fatichah

2015

Lecture Notes in Electrical Engineering

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Abstract.A hybrid technique combining the AdaBoost ensemble method with the neural network with fuzzy membership function (NEWFM) method is proposed for medical data classification and disease diagnosis. Combining the Adaboost, a general method used to improve the performance of learning methods, with the 'standard' NEWFM, which uses as base classifiers, ensures better accuracy in medical data classification tasks and diagnosis of diseases. To validate the proposal, four medical datasets related to epileptic seizure detection, Parkinson, cardiovascular (heart), and hepatitis disease diagnoses were used. The results show an average classification accuracy of 95.8% (made up of best accuracy of 99.5% for epileptic seizure, 87.9% for Parkinson, 97.4% for cardiovascular (heart) disease, and 98.7% for Hepatitis dataset classifications), which suggests that the proposed technique is capable of efficient medical data classification and potential applications in disease diagnosis and treatment.

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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%

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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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Hybrid Metaheuristics for Medical Data Classification

Cited by 15 publications

References 72 publications

CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure

A Combined AdaBoost and NEWFM Technique for Medical Data Classification

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

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