An effective clinical decision support system using swarm intelligence

As healthcare organizations collect a large volume of data on a daily basis, there is an absolute necessity to extract valuable information from them, owing to the importance and the time-sensitiveness of the industry. Although the healthcare sector has come up with several new computer technologies, the industry actually lags for an efficient approach to medical diagnosis. Hence, performing an accurate prediction of the patients’ medical problem through the use of an effective automated system comes in place. As in the recent survey, most of the medical research has explored predictive analytics for its performance efficiency; the proposed work uses the same for effective learning of medical data. The work proposes a novel filter-based feature selection method using a variant of a meta-heuristic search strategy. The experimental results show that the method is comparatively better than the existing filter-based feature selection methods and exclusively handles the imbalanced medical datasets using newly devised fitness functions.