Applicability of classifier to discovery knowledge for future prediction modelling

Publication Type:
Journal Article
Journal of Ambient Intelligence and Humanized Computing, 2022
Issue Date:
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The immense growth of new technological interventions has forced researchers and scientists around the globe to adopt the widely anticipated technology of Machine Learning (ML) and Artificial Intelligence (AI). ML and AI have generously prospected itself from the past decade in the discovery of knowledge from databases. Several ML and AI based adoptive technologies have emerged in varied application domains, and are thus widely opening a new era of knowledge in decision making. Moreover, ML and AI are techniques that can improve the treatment and diagnosis of diseases. In the current study, we have designed and deployed a “PROCLAVE”. The tool was designed in varied layers of structure, where each layer plays a significant role in determining the patterns. We have applied several libraries for the processing of a prototype to develop a visualization interface. The tool forecasts health vulnerability, makes a comparison among variable classifiers and visualize the results for end users. Moreover, the proposed architecture is based on the concepts of conceptualization and visualization to detect the overall dashboard. Furthermore, the current approach was synthesized and populated with a database that allows the end users to select the variable features and relatively determine the interactive patterns for the number of cases. The database was collected from the National Institute of Health Stroke (NIHS) in the United States. Data was gathered for stroke patients who were diagnosed with stroke from 1950 to 2015. The study was based on several attributes which included causes of death, sex, race, Hispanic origin and others to discover unknown patterns for future decision making.
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