A Combined AdaBoost and NEWFM Technique for Medical Data Classification

Iliyasu, Abdullah M.; Fatichah, Chastine

doi:10.1007/978-3-662-46578-3_95

Cited by 11 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in proximity) within the same cluster [21]. Taking the co-occurrences of pairs of patterns in the same cluster as votes for their association, the data partitions produced by multiple runs of the K-means algorithm are mapped into an n×n co-association matrix [22] using equation (1).…”

Section: Evidence Combinationmentioning

confidence: 99%

“…The disease diagnoses procedures involve the use of data from of a variety of sources within the health care records, such as the health condition notes, results of laboratory, radiological and pathological tests and examinations, as well as results from numerous other sources [1]. These health records of patients are collated into a dataset that is later used to make initial diagnosis of new patients.…”

Section: Introductionmentioning

confidence: 99%

“…These health records of patients are collated into a dataset that is later used to make initial diagnosis of new patients. This use of medical datasets for disease diagnosis based on available training/examples data from health records of patients is called a medical data classification task [1]. Medical data classification tasks are executed using different varieties of data types including text, signal, image, DNA, voice, etc.…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 4 more Smart Citations

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

Iliyasu

Fatichah

2016

Automatika

Self Cite

View full text Add to dashboard Cite

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...

show abstract

Section: Evidence Combinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%