A novel intelligent approach for predicting atherosclerotic individuals from big data for healthcare

Abstract

Atherosclerosis is a condition in human circulatory, where the arteries become narrowed and hardened due to accumulation of plaque around artery wall. The growth of the disease is slow and asymptomatic. Currently, imaging methods are applied for predicting the disease progression; however, they are deficient in the required resolution and sensitivity for detection. In this work, clinical observations and habits of individuals are considered for assorting the pathologic community. Intelligent machine learning technique, decision tree forest is used for assorting the individuals. A case study was made in this work regarding the atherosclerosis disease progression and crucial features were extracted. Optimised missing value imputation strategy, iterative principal component analysis for STULONG data-set and efficient feature subset selection method, hybrid fast correlation-based filter (FCBF) have been employed for extracting the relevant features and ignoring the redundant features. Further proceeding with the methodology, our work has outperformed with extreme overall accuracy of about 99.47% compared with other state-of-the-art machine learning techniques.

Big data analytics business value and firm performance: linking with environmental context

“…Concerning their time horizon and nature, a big data application can have a past, hence descriptive, orientation, such as in auditing solutions. Otherwise a big data application can have a present and predictive orientation (Lee, 2017;Priya and Ranjith Kumar, 2015;van der Spoel et al, 2017), such as real-time trading tools. Finally, a big data application can have a future, hence prescriptive (Amankwah-Amoah, 2016), orientation for example in strategic decision-support systems (Bi et al, 2019a(Bi et al, , 2019bGunasekaran et al, 2017).…”

Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review

“…Chong et al (2015) modelled variables that influence the adoption of IoT (e.g., RFID) in healthcare. Priya and Ranjith Kumar (2015) used big data analytics to predict the progression of atherosclerotic disease, the narrowing and hardening of arteries due to the accumulation of plaque on the artery wall. Fan et al (2018) recognised the factors affecting the adoption of artificial intelligence-based medical diagnosis support systems.…”

A bibliographic study on big data: concepts, trends and challenges

“…This initiative involved six federal government agencies; Department of Defense, Defense Advanced Research Projects Agency, Department of Energy, National Institutes of Health, National Science Foundation, and US Geological Survey (Kalil, 2012). In addition, scholars have attempted to identify its role in health care (Priya and Ranjith Kumar, 2015;Wu et al, 2015;Huang et al, 2015;Qin et al, 2015) and supply chain and logistics (Zhong et al, 2015(Zhong et al, , 2017Schoenherr and Speier-Pero, 2015;Dubey et al, 2015). For a detailed overview of big data research, readers may refer to Table AI.…”