An Integrated Nature-Inspired Algorithm Hybridized Adaptive Broad Learning System for Disease Classification

Abstract

Classification of different deadly diseases using machine learning algorithms helps in making the health care system more robust. This, not only reduces the human errors during diagnosis of disease due to inexperience but also helps the physician for taking an emergency action earlier to biopsy. As the genomic data undergoes through the malediction of excessive dimension problem, both the selection of the remarkable genes and classification of these data efficiently still remain as a demanding research problem. To obtain the notable features from high dimensional data, by using a nature-inspired algorithm, is a Non-deterministic Polynomial-time (NP)-Hard problem. Therefore, a researcher can apply new algorithm to solve this issue. In this suggested approach, an integrated natured-inspired algorithm i.e., Sine-Cosine (SC) based Monarch Butterfly Optimization (SC-MBO) is merged with an Adaptive Broad Learning System (ABLS) called SC-MBO-ABLS, to find out the most notable genetic features and classify the genomic data at the same time. In the preliminary stage, a feature extraction method i.e. Kernel based Fisher Score (K-FS) is applied to extract a notable key gene subset. Then, this extracted key gene subset goes through further execution with the SC-MBO-ABLS method. To examine the effectiveness of the presented method, ten genomic datasets are considered. Here, several performance evaluators (i.e., Precision, Matthews Correlation Coefficient, Sensitivity, Kappa, Specificity, and F-score) are used for a neutral estimation of the presented approach. This presented model is compared with SC-MBO wrapped Multilayer Perceptron (SC-MBO-MLP), SC-MBO wrapped Extreme Learning Machine (SC-MBO-ELM), and SC-MBO wrapped Kernel Extreme Learning Machine (SC-MBO-KELM). Further, sixteen existing standard models are compared to prove the supremacy of the presented method. Moreover, an analysis of Variance i.e., ANOVA test is carried out for statistical evaluation of the proposed work. Eventually, according to the above quantitative and qualitative measure, it is summarized that the suggested SC-MBO-ABLS method surpasses other considering standard models.

Keywords

• Genomic data
• high dimensionality
• notable genes
• feature extraction
• kernel fisher score
• classification