Comparative Descriptive Analysis of Breast Cancer Tissues Using K-means and SelfOrganizing Map

Abstract

Data mining is a descriptive and predictive data analytical technique that discovers meaningful and useful knowledge from dataset. Clustering is one of the descriptive analytic techniques of data mining that uses latent statistical information that exists among dataset to group them into meaningful and or useful groups. In clinical decision making, information from medical tests coupled with patients' medical history is used to make recommendations, and predictions. However, these voluminous medical datasets analysis is always dependent of individual analyzer that might have in one way or the other introduced human error. In other to solve this problem, many automated analyses have been proposed by researchers using various machine learning techniques and various forms of dataset. In this paper, dataset from electrical impedance imaging of breast tissues are clustered using two unsupervised algorithms (k-means and self-organizing map). Result of the performances of these machine learning algorithms as implemented with R i368 version 3.4.2 shows a slight outperformance of K-means in terms of classification accuracy over self-organizing map for the considered dataset.

Homogeneous Adaboost Ensemble Machine Learning Algorithms with Reduced Entropy on Balanced Data

“…The aforementioned dataset includes highlights found from FNA test sweeps on a bosom growth. The informative index is divided into two parts for ML calculations: 70% for preparation and 30% for testing [13]. Their findings revealed that all of the ML computations introduced a better performance on the twofold order of carcinomas, such as determining whether growth is safe or harmful.…”