Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Abstract

Recently, medical data classification becomes a hot research topic among healthcare professionals and research communities, which assist in the disease diagnosis and decision making process. The latest developments of artificial intelligence (AI) approaches paves a way for the design of effective medical data classification models. At the same time, the existence of numerous features in the medical dataset poses a curse of dimensionality problem. For resolving the issues, this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data (FSS-AICBD) technique. The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results. Primarily, the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity. In addition, the information gain (IG) approach is applied for the optimal selection of feature subsets. Also, group search optimizer (GSO) with deep belief network (DBN) model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm. The choice of IG and GSO approaches results in promising medical data classification results. The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets. The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

Knowledge atlas on the medical artificial intelligence research: a bibliometric analysis based on CiteSpace and VOSviewer

“…The results show that the performance of this AI method in ultrasound breast cancer classification has been significantly improved, and it can significantly improve the early diagnosis of young women's breast cancer. Scholars Alzahrani J S [71] introduced a feature subset selection technology (FSSAICBD) based on AI biomedical data classification model technology, which solved the dimension disaster problem caused by many features in medical data sets. Anand L [72] proposed a new hybrid model for liver syndrome classification, which analyzed the medical data of patients through hybrid artificial neural network, and classified the medical records for the possibility of liver diseases, which was helpful for early diagnosis and prevention of liver diseases.…”

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