Hybridization of Machine Learning Model with Bee Colony based Feature Selection for Medical Data Classification

Abstract

Nowadays, an important count of biomedical data is created continuously in several biomedical equipment and experiments because of quick technical enhancements in biomedical science. The study of clinical and health data is vital to enhance the analysis precision, prevention, and treatment. Initial analysis and treatment are extremely important approaches for preventing deaths in many diseases. Accordingly, the data mining and machine learning (ML) approaches are helpful tools for utilizing minimization error and for providing helpful data for analysis. But the data obtained in digital machines takes higher dimensionality, and not every data attained in digital machines is significant to specific diseases. This article develops an artificial bee colony-based feature selection with optimal hybrid ML model for medical data classification (ABCFS-OHML) technique. The presented ABCFS-OHML technique mainly aims to identify and classify the presence of disease using medical data. To attain this, the presented ABCFS-OHML technique initially pre-processes the input data in two ways namely null value removal and data transformation. Furthermore, the presented ABCFS-OHML technique uses ABCFS model for the choice of effectual subset of features. At last, root means square propagation with convolutional neural network-Hop field neural network (CNN-HFNN) model for classification purposes. The usage of RMSProp optimizer assists in attaining optimal hyperparameter selection of the CNN-HFNN method. The performance validation of the ABCFS-OHML technique takes place using three medical datasets. The comparison study reported that the ABCFS-OHML technique has accurately classified the medical data over other recent approaches.