Czasopismo | PRZEGLĄD ELEKTROTECHNICZNY | Rocznik 2024 - zeszyt 1

A review of homogenous ensemble methods on the classification of breast cancer data

Przegląd jednorodnych metod zespołowych dotyczących klasyfikacji danych dotyczących raka piersi

10.15199/48.2024.01.21

nr katalogowy: 147101
10.15199/48.2024.01.21

Streszczenie

In the last decades, emerging data mining technology has been introduced to assist humankind in generating relevant decisions. Data mining is a concept established by computer scientists to lead a secure and reliable classification and deduction of data. In the medical field, data mining methods can assist in performing various medical diagnoses, including breast cancer. As evolution happens, ensemble methods are being proposed to achieve better performance in classification. This technique reinforced the use of multiple classifiers in the model. The review of the homogenous ensemble method on breast cancer classification is being carried out to identify the overall performance. The results of the reviewed ensemble techniques, such as Random Forest and XGBoost, show that ensemble methods can outperform the performance of the single classifier method. The reviewed ensemble methods have pros and cons and are useful for solving breast cancer classification problems. The methods are being discussed thoroughly to examine the overall performance in the classification.

Abstract

W ostatnich dziesięcioleciach wprowadzono nową technologię eksploracji danych, która ma pomóc ludzkości w podejmowaniu odpowiednich decyzji. Eksploracja danych to koncepcja opracowana przez informatyków w celu zapewnienia bezpiecznej i niezawodnej klasyfikacji i dedukcji danych. W medycynie metody eksploracji danych mogą pomóc w przeprowadzaniu różnych diagnoz medycznych, w tym raka piersi. W miarę ewolucji proponuje się metody zespołowe, aby uzyskać lepszą skuteczność klasyfikacji. Technika ta wzmocniła zastosowanie w modelu wielu klasyfikatorów. Przeprowadzany jest przegląd jednorodnej metody zespołowej klasyfikacji raka piersi w celu określenia ogólnej skuteczności. Wyniki recenzowanych technik zespołowych, takich jak Random Forest i XGBoost, pokazują, że metody zespołowe mogą przewyższać skuteczność metody pojedynczego klasyfikatora. Omówione metody zespołowe mają zalety i wady i są przydatne w rozwiązywaniu problemów związanych z klasyfikacją raka piersi. Metody są szczegółowo omawiane w celu sprawdzenia ogólnej wydajności w klasyfikacji.

Słowa kluczowe

Keywords

Bibliografia

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