As the big data is growing in biomedical and healthcare communities, so are precise analyses of medical data aids, premature disease identification, patient care as well as community services. On the other hand, the accuracy of the analysis decreases, if t he medical data quality is imperfect. As a result, the choice of features from the dataset turns out to be an extremely significant task. Feature selection has exposed its efficiency in numerous applications by means of constructing modest and more comprehensive models, enlightening learning performance and preparing clean and clear data. The proposed method analyzes the difficulties of feature selection for big data analytics. Improved Ant Colony Optimization based Feature Selection (IACO) algorithm is presented for resolving this issue. The reconstruction of missing data before the incomplete data available was performed with help of latent factor mode. Therefore, it was not easy t o choose the best features from the structured and unstructured data. the unheard technique which is called Weighted Ensemble Based Neural Network for multimodal disease risk prediction(WENN-MDRP) algorithm is implemented in order to provide the best features selection among structured as well as unstructured data. The research method provides improved prediction accuracy when matched with conventional techniques. In the MATLAB environment, the presented classifiers are implemented. The outcomes are computed in regard to reca ll, precision, accuracy, f-measure and error rate.
The healthcare informatics focuses on health data, information and knowledge, including their collection, processing, analysis and use bioinformatics employs computational tools and techniques to study and analyze large biological databases and to understand disease and study of inherent genetic information molecular structure by relating them with healthcare data. This amassing of healthcare information will enable the biologist and scientist to improve health as drug discovery. This paper touches on big data in healthcare and analyzes of those big data in healthcare for the better improvement of healthcare system, bioinformatics data stored in secured manner. Finally, the paper looks on the helpful result, the beneficence of each of them in amelioration of healthcare system. To achieve this health amelioration in bioinformatics, we use Hadoop as tools which collect and analyze the huge amount of data in healthcare system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.