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

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.