期刊
EGYPTIAN INFORMATICS JOURNAL
卷 19, 期 3, 页码 179-189出版社
CAIRO UNIV, FAC COMPUTERS & INFORMATION
DOI: 10.1016/j.eij.2018.03.002
关键词
Chronic disease; Feature selection; Traditional systems; Disease diagnosis; Parallel classification systems; Adaptive classification systems
Chronic Disease Prediction plays a pivotal role in healthcare informatics. It is crucial to diagnose the disease at an early stage. This paper presents a survey on the utilization of feature selection and classification techniques for the diagnosis and prediction of chronic diseases. Adequate selection of features plays a significant role for enhancing accuracy of classification systems. Dimensionality reduction helps in improving overall performance of machine learning algorithm. The application of classification algorithms on disease datasets yields promising results by developing adaptive, automated and intelligent diagnostic systems for chronic diseases. Parallel classification systems can be used to expedite the process and to enhance the computational efficiency of results. This work presents a comprehensive overview of various feature selection methods and their inherent pros and cons. We then analyze adaptive classification systems and parallel classification systems for chronic disease prediction. (C) 2018 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information, Cairo University.
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