BioExpDNN: Bioinformatic Explainable Deep Neural Network

In recent years, machine learning is applied in the bioinformatics and medical fields to analyze relationships among biological features and behaviors. However, it is difficult to discover the significant features of large-scale datasets. A novel feature extraction method called bioinformatic explainable deep neural network (BioExpDNN) is proposed to filter the critical features with strong influences on the dataset and to explain the interaction of features. In the practical experiments, this study adopted three biomedical science datasets from the UCI (University of California, Irvine) Machine Learning Repository: (1). Cryotherapy Data Set (CDS) contains 6 attributes and 2 classes (i.e., recovery and non-recovery); (2). Cervical Cancer Behavior Risk Data Set (CCBRDS) consists of 18 attributes and 2 classes (i.e., cervical cancer patient and healthy body); (3). Heart Failure Clinical Records Data Set (HFCRDS) includes 12 clinical attributes and 2 classes (i.e., death and life). In comparison results, extracted features were considered as inputs of a classifier based on deep neural network for classification. The classification accuracy was selected as an evaluation factor to evaluate the performance of feature extraction methods. The experimental results showed that the classification accuracies of CDS, CCBRDS, and HFCRDS were 92.59%, 100%, and 78.9%, respectively.