Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification - Mohammed, Alhassan Afnan (2022)
Heart disease classification is considered a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but achieving higher classification accuracy is a great challenge in the medical sector due to the presence of noisy, and high-dimensional data. Fuzzy clustering-based filtering methods are introduced for essential feature selection. From the selected features, deep learning has become an important stage for disease diagnosis. However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. Deep Belief Network (DBN) is a sophisticated learning system that requires a high level of approach and executes well. The major contribution of this research is to introduce a Taylor-Bird Swarm optimization-based Deep Belief Network (Taylor-BSA-based DBN) for medical data classification. Firstly, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection and can be utilized for handling high-dimensional data. Then, the selected features are given as input to the DBN classifier which is trained using the Taylor-based bird swarm algorithm (Taylor-BSA). Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA).