Medical Dataset Classification Using Ensemble Feature Selection and Back Propagation Neural Network Algorithm - T. Christopher (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Due to the ongoing creation of digital data, the amount of medical data has significantly expanded in recent years. the many types of medical information, including reports, text, numbers, monitoring, and laboratory results. Because of a problem with a single optimisation technique in the current system, classification accuracy is not considerably guaranteed. Another significant issue is error rates, which prevents early illness prediction from being carried out effectively. This research work uses EFS (Ensemble Feature Selection) with BPNN (Back Propagation Neural Networks) to handle the afore mentioned issues. The input data is pre-processed using KMC (K-Means Clustering) algorithm, mainly for handling missing values and subsequently, EFS method is used to choose the features since it produces the best fitness values using an objective function. To solve the FS problem, EFS relies on integrating many FS rather than just one FS. Combining the results of multiple single FS approaches, such as EEHO (Entropy Elephant Herding Optimisation) and AFOA (Adaptive Firefly Optimisation Algorithm), is one alternative for the EFS method. And EBFO (Entropy Butterfly Optimization Algorithm) acquire improved outcomes rather than utilizing a single FS methodology. Finally, the medical dataset classification is performed using BPNN algorithm. With the help of the BPNN algorithm, a multilayer FFNN (feed forward neural networks) is trained. The class labels in tuples are predicted using weights that are learnt iteratively. The experimental findingsof the proposed EFS-BPNN algorithm demonstrates better values for accuracy, sensitivity, specificity, and execution time when compared with existing methods.