Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data

Abstract

Cancer disease is accountable for many deaths that are over 9.6 million in 2018 and roughly one out of six deaths occur because of cancer worldwide. The colon cancer is the second prominent source of death of around 1.8 million cases. This research is inclined to detect the colon cancer from microarray dataset. It will aids the experts to distinguish the cancer cells from normal cells for appropriate determination and treatment of cancer at earlier stages that leads to increase the survival rate of the patients. The high dimensionality in microarray dataset with less samples and more attributes creates lag in the detection capability of the classifier. Hence there is a need for dimensionality reduction techniques to preserve the significant genes that are prominent in the disease classification. In this article, at first ANOVA method used to select the best genes and then principal component analysis (PCA) and fuzzy C‐means clustering (FCM) techniques are further employed to choose relevant genes. The PCA and FCM features are classified using model, discriminant, regression, hybrid, and heuristic‐based classifiers. The attained results show that the heuristic classifier with PCA features is encapsulated an average classification accuracy of 97.92% for classifying both the colon cancer and normal samples. Also, for FCM features, the Heuristic classifier is maintained at an average classification accuracy of 99.48% and 97.92% for classifying the colon cancer and normal samples, respectively. The Heuristic classifier outperforms with high accuracy than all other classifiers in the classification of colon cancer.

Optimized cancer detection on various magnified histopathological colon images based on DWT features and FCM clustering

“…The extracted functions consist of a lot of information mixing with individual differences, atmosphere noise, and other irrefutable noise, in most actual cases with image processing techniques. Thus to reduce the feature, FCM based feature reduction is considered [34]. This approach eliminates the uncertainty of features extracted and is perfect for the prediction of the tumor.…”

Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression

“…The authors used mutual information theories and spectral graph for selecting gene from expression data that has a small sample size with high dimensionality. In the other recent works proposed in Nirmalakumari et al., 15 Raj and Mohanasundaram, 16 Bentkowska, 17 and Santhakumar and Logeswari, 18 the authors have been presented some solutions for this research issue in gene expression of microarray data.…”