Abstract

Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, 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 to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively.

Highlights

  • Contemporary medicine depends on a large amount of information accumulated in medical datasets

  • The selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection

  • Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively

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Summary

Introduction

Contemporary medicine depends on a large amount of information accumulated in medical datasets. The extraction of such constructive knowledge can help when making scientific decisions to diagnose disease. Medical data can enhance the management of hospital information and endorse the growth of telemedicine. Medical data primarily focuses on patient care first, and research resources second. The main rationalization to collect medical data is to promote patient health conditions [1]. The accessibility of numerous medical data causes redundancy, which requires effectual and significant techniques for processing data to extract beneficial knowledge. Cloud-based services can assist with managing medical data, including compliance management, policy integration, access controls, and identity management [4]

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