Izvorni znanstveni članak
https://doi.org/10.7305/automatika.2016.10.1427
Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis
Abdullah M. Iliyasu
; Computational Intelligence & Intelligent Systems (CIIS) Research Group, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Kingdom of Saudi Arabia
Chastine Fatichah
; Department of Informatics, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya 60111, Indonesia
Khaled Abuhasel
; Department of Mechanical Engineering, Bisha University, Bisha 61361, Kingdom of Saudi Arabia
Sažetak
Traditionally, 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 C-Means (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.
Ključne riječi
Evidence accumulation clustering; K-means; fuzzy C-means; possibilitic fuzzy C-means; hybrid intelligent systems; health informatics; medical data classification; disease diagnosis
Hrčak ID:
180723
URI
Datum izdavanja:
23.3.2017.
Posjeta: 1.086 *