An integrated dimensionality reduction model for classifying IoT-enabled smart healthcare genomic data

Abstract

Machine Learning (ML) is now a powerful factor in everyday life, and in most fields that we desire to improve. ML is a field for creating systems that can learn from data, whether labeled or unlabeled, or from the ambient. ML is employed in various of disciplines, but it is incredibly useful in the healthcare sector since it leads to improved decision-making and prediction approaches. Since ML in healthcare services is scientific research, we need to save, obtain, and properly utilize data, knowledge, and provide expertise to the issues that face the healthcare industry, as well as learning for proper decision-making. Owing to most of these innovations there is indeed a big development in health care sectors over the decades. Healthcare analysis of data has become one of the greatest favorable research fields. Healthcare includes data from diverse kinds with medical data, functional genomic data, in addition sensor data, obtained through involvement of various wearable and wireless sensor devices. Manually processing this relevant data is really challenging. ML has developed as a crucial data analysis technique. Health professionals employ these tools and techniques to analyze healthcare data to identify hazards as well as provide effective diagnosis and management. In this study, a dimensionality reduction model is suggested for classifying IoT enabled healthcare data analysis, using a COVID-19 dataset with partial least square (PLS), linear discriminant analysis (LDA) on support vector machine (SVM) and Kth nearest neighbor (KNN) classifiers. This study uses COVID-19 dataset, ML approach such as PLS, LDA, SVM, and KNN on a MATLAB tool for the analysis.The result obtained shows that PLS-LDA-SVM outperformed PLS-LDA-KNN with 90% accuracy. The review of this study has proven that this study is efficient for decision making by practitioners to adopt for efficient analysis of healthcare data analysis.