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A Hybrid Multiple Indefinite Kernel Learning Framework for Disease Classification from Gene Expression Data

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 844-855页

作者： Swetha, S. Srinivasan, G. N. Dayananda, P. RV Coll Engn Dept Informat Sci & Engn Bengaluru 560059 Karnataka India Manipal Acad Higher Educ Manipal Inst Technol Bengaluru Dept Informat Technol Manipal India

In recent years, machine Learning (ML) techniques have been used by several researchers to classify diseases using gene expression data. Disease categorization using heterogeneous gene expression data is often used for defining critical problems such as cancer analysis. A variety of evaluated factors known as genes are used to characterize the gene expression data gathered from DNA microarrays. Accurate classification of genetic data is essential to provide accurate treatments to sick people. A large number of genes can be viewed simultaneously from the collected data. However, processing this data has some limitations due to noises, redundant data, frequent errors, increased complexity, smaller samples with high dimensionality, difficult interpretation, etc. A model must be able to distinguish the features in such heterogeneous data with high accuracy to make accurate predictions. So this paper presents an innovative model to overcome these issues. The proposed model includes an effective multiple indefinite kernel learning based model for analyze the gene expression microarray data, then an optimized kernel principal component analysis (OKPCA) to select best features , hybrid flow-directed arithmetic support vector machine (SVM)-based multiple infinite kernel learning (FDASVM-MIKL) model for classification. flow direction and arithmetic optimization algorithms are combined with SVM to increase classification accuracy. The proposed technique has an accuracy of 99.95%, 99.63%, 99.60%, 99.51% , 99.79% using the datasets including colon, Isolet, ALLAML, Lung_cancer, and Snp2 graph.

关键词： Gene expression optimized kernel principle component analysis multiple indefinite kernel learning flow direction algorithm based support vector machine arithmetic optimization algorithm

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