Flock optimization induced deep learning for improved diabetes disease classification

Abstract

AbstractDiabetic disease classification requires a precise understanding of the clinical inputs and their intensity as observed through different stages. Automated and machine‐centric classification requires validated data handling and non‐converging inputs. For improving the classification precision impacted due by complex computations, this article introduces an assimilated method incorporating flock optimization and conventional deep learning. Deep learning trains the classification system through the best‐fit solution generated by the flock optimization. The features from the input data are first identified for which an initial population is initiated. The identified features are classified based on their leap‐up behaviour; this behaviour is induced if the data feature modifies the actual representation. If the data feature shows up over‐fitting behaviour, then it is classified as abnormal and is discarded. Therefore the objective function is to identify the best‐fitting data feature from the maximum flock members showing similar leap‐up behaviour. This output is used for training the deep learning paradigm for classifying precision‐less and high features. The precision is determined using existing classified data that matches better the flock output. If the classified data is under less precision, then the leap‐up behaviours' objective is tuned to eliminate over‐fitting inputs. Therefore, the variable features are thwarted for preventing precision degradation for varying diabetics' clinical observed data. The introduced system maximize the recognition accuracy by 8.47% and minimize the complexity by 7.65%.