Toward a High-Accuracy Hybrid System for Cardiac Patient Data Analysis using C-Means Fuzzy Clustering in Neural Network Structure
محورهای موضوعی :
Majlesi Journal of Telecommunication Devices
Mahmood Karim Qaseem
1
,
Razieh Asgarnezhad
2
1 - Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
2 - Department of Computer Engineering, Aghigh Institute of Higher Education Shahinshahr, 8314678755, Isfahan, Iran
تاریخ دریافت : 1401/10/21
تاریخ پذیرش : 1402/01/08
تاریخ انتشار : 1402/03/11
کلید واژه:
High Accuracy,
Hybrid system,
Neural Network Structure,
Data Analysis,
cardiac patients,
C-Means Fuzzy Clustering,
چکیده مقاله :
The main problem related to heart disease is the lack of timely diagnosis or the general weakness in the diagnosis of this disease, which is also due to the lack of selection of the appropriate model by the doctor or the lack of proper use of standard models. One of the essential applications of data mining techniques is related to medicine and disease diagnosis. One of the data mining techniques is information clustering. This paper will try to provide a model for the diagnosis of heart disease and its improvement in terms of accuracy on the standard UCI heart database. In this research, with a comprehensive and complete review of the C-Meaning fuzzy clustering method and neural networks in the field of heart disease prediction, an attempt is made to improve these solutions and provide new solutions in this field. The main goal is to combine these two data mining algorithms, both of which alone showed the highest accuracy and the fastest speed in past research. The current authors are trying to find a model that has higher accuracy and speed than the previous methods and makes fewer mistakes and has significantly higher efficiency than other models. The numerical tests implemented on the proposed model show the superiority of the new model compared to the conventional methods in the literature.
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