Deep learning approach for microarray cancer data classification

• 作者: Guesh Dagnew Hema Shekar Basavegowda
• 来源期刊: 智能技术学报
• 学科: 医学
• 关键词: MICROARRAY DIAGNOSIS dimensionality
• 年，卷（期）: 2020,（1）
• 页码范围: 22-33
• 页数: 12页
• 分类号: R73

摘要

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.