OPTIMAL ENSEMBLE FEATURE SELECTION (OEFS) METHOD AND KERNEL WEIGHT CONVOLUTIONAL NEURAL NETWORK (KWCNN) CLASSIFIER FOR MEDICAL DATASETS

Abstract

Disease detection software that works automatically in healthcare domain refers to the proactive or reactive use of computerised data systems for diagnosis of diseases. Medical knowledge base, data processing, and data analytics are the three major components of the system. The procedures of data processing and data analytics are crucial. Data mining (DM) techniques were used to process these processes. DM is a tool for finding patterns in massive amounts of data and retrieving knowledge. Clinical and diagnostic evidence has created a slew of reliable timely detection services and other health-related technology in the DM and healthcare industries. Artificial Intelligence (AI) in Machine Learning (ML) includes classification and predictive analytics. Identifying key characteristics and developing a classification model to determine whether the cases are disease or not is a difficult task. Feature selection (FS) refers to the process of reducing the quantity of input features when developing a predictive model. Reducing the number of input features is desirable because it cuts the computational cost of modelling while also improving the model’s performance in some cases. Instead of using a single feature selection, Optimal Ensemble Feature Selection (OEFS) solves a feature selection problem by integrating numerous feature selections. The OEFS method works by integrating the outputs of different single feature selection models like Divergence Weight Elephant Herding Optimization (DWEHO), Divergence Weight ButterFly Optimization Algorithm (DWBFO), and Differential Evolution (DE). By merging different subsets of features, Weighted Majority Voting (WMV) is used in finding the optimal feature subset. Classification model using Kernel Weight Convolutional Neural Network (KWCNN) classification is proposed. The convolution operation is a mathematical linear action across matrices that gives it its name. In terms of medical disease diagnosis, the proposed KWCNN classification performs quite well. To determine the performance of all classification algorithms, evaluation criteria such as sensitivity, specificity, f-measure, and accuracy were measured using a confusion matrix.

Keywords

• medical diseases
• healthcare
• databases
• data mining (dm)
• artificial intelligence (ai)
• machine learning (ml)
• optimal ensemble feature selection (oefs)
• divergence weight elephant herding optimization (dweho)
• divergence weight butterfly optimization algorithm (dwbfo)
• differential evolution (de)