A Hybrid Scheme for Heart Disease Diagnosis Using Rough Set and Cuckoo Search Technique
| Autor: | D. P. Acharjya, Kauser Ahmed P |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Reduct
Scheme (programming language) Blood Glucose Support Vector Machine 020205 medical informatics Heart Diseases Computer science Medicine (miscellaneous) Health Informatics Feature selection Blood Pressure 02 engineering and technology computer.software_genre Electrocardiography Sex Factors Health Information Management Fuzzy Logic 0202 electrical engineering electronic engineering information engineering Information system Humans Diagnosis Computer-Assisted Cuckoo search computer.programming_language Age Factors Decision rule Cholesterol Rough set Data mining Raw data computer Algorithms Information Systems |
| Zdroj: | Journal of medical systems. 44(1) |
| ISSN: | 1573-689X |
| Popis: | Large volumes of raw data are created from the digital world every day. Acquiring useful information from these data is challenging, and it turned into a prime zone of momentum explore. More research is done in this direction. Further, in disease diagnosis, many uncertainties are involved in the information system. To handle such uncertainties, intelligent techniques are employed. In this paper, we present an integrated scheme for heart disease diagnosis. The proposed model integrates cuckoo search and rough set for inferencing decision rules. At the underlying phase, we employ a cuckoo search to discover the main features. Further, these main features are analyzed using rough set generating rules. An empirical analysis is carried out on heart disease. Besides, a comparative study is also presented. The comparative study demonstrates the feasibility of the proposed model. |
| Databáze: | OpenAIRE |
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