PCA based feature extraction and MPSO based feature selection for gene expression microarray medical data classification

In this paper, a novel Multi Class based Feature Extraction (MC-FE) method has been proposed for medical data classification. Genomic datasets, or gene expression-based microarray medical datasets, are categorised for cancer diagnosis. The first stage involves applying a feature extraction technique. The Principal Component Analysis (PCA) is used to extract the features for medical data classification to detect leukemia, colon tumors, and prostate cancer. The MPSO (modified particle swarm optimization) technique is used at the second stage to pick features from high-dimensional microarray medical datasets like prostate cancer, leukemia, and colon tumors. Finally, SVM, KNN, and Naive Bayes classifiers are used to classify medical data. Then, the classified data are stored in the cloud IOT sensors. When compared to the existing methods, the proposed method gives better optimized results for SVM, KNN, and Naive Bayes of 0.88, 0.86, and 0.73, respectively. The results of cancer data extraction and feature selection are also contrasted and assessed using certain performance measure factors.