Deep learning approach for microarray cancer data classification. Issue 1 (30th January 2020)

• Record Type: Journal Article
• Authors: Basavegowda, Hema Shekar; Dagnew, Guesh
• Is Part Of: CAAI transactions on intelligence technology. Volume 5:Issue 1(2020)
• Page Start: 22
• Page End: 33
• Publication Date: 2020-01-30
• Subjects: pattern classification -- entropy -- biology computing -- cancer -- learning (artificial intelligence) -- principal component analysis -- neural net architecture -- minimax techniques -- feature extraction -- lab‐on‐a‐chip; deep learning approach -- microarray cancer data classification -- feature values -- deep feedforward method -- 7‐layer deep neural network architecture -- principal component analysis -- min–max approach -- binary cross‐entropy -- adaptive moment estimation -- dimensionality reduction technique; C1140Z Other topics in statistics -- C1180 Optimisation techniques -- C5290 Neural computing techniques -- C6130 Data handling techniques -- C7330 Biology and medical computing; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0028
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Analysis of microarray data is a highly challenging problem due to the inherent complexity in the nature of the data associated with higher dimensionality, smaller sample size, imbalanced number of classes, noisy data‐structure, and higher variance of feature values. This has led to lesser classification accuracy and over‐fitting problem. In this work, the authors aimed to develop a deep feedforward method to classify the given microarray cancer data into a set of classes for subsequent diagnosis purposes. They have used a 7‐layer deep neural network architecture having various parameters for each dataset. The small sample size and dimensionality problems are addressed by considering a well‐known dimensionality reduction technique namely principal component analysis. The feature values are scaled using the Min–Max approach and the proposed approach is validated on eight standard microarray cancer datasets. To measure the loss, a binary cross‐entropy is used and adaptive moment estimation is considered for optimisation. The performance of the proposed approach is evaluated using classification accuracy, precision, recall, f ‐measure, log‐loss, receiver operating characteristic curve, and confusion matrix. A comparative analysis with state‐of‐the‐art methods is carried out and the performance of the proposed approach exhibit better performance than many of the existing methods.