Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine
In recent years, machine learning algorithms are widely used for diagnosis and timely treatment of diseases. Moreover, diagnosis of disease on early stages is very effective in improving the disease and in reducing the cost of treatment for the patient. Heart disease is one of the main causes of dea...
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University of Qom
2020-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1277_0034b961ec78c93b97ef0ba0714e9a8a.pdf |
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| author | Zeinab Hassani Mahin Khosravi |
| author_facet | Zeinab Hassani Mahin Khosravi |
| author_sort | Zeinab Hassani |
| collection | DOAJ |
| description | In recent years, machine learning algorithms are widely used for diagnosis and timely treatment of diseases. Moreover, diagnosis of disease on early stages is very effective in improving the disease and in reducing the cost of treatment for the patient. Heart disease is one of the main causes of death in the world. Several studies have been conducted to diagnose of disease and to design an intelligent and efficient system. In this paper, a hybrid algorithm of Whale Optimization Algorithm and simulated annealing are presented to identify the effective factors in the diagnosis of the disease. The support vector machine algorithm is considered for effective classification of the disease. The proposed approach is evaluated using the Cleveland Heart Disease Data Collection in the UCI database. The proposed algorithm has obtained with an accuracy of 87.78% which is able to diagnose of disease with fewer attributes. The results exhibition the superiority of the proposed method which the proposed approach can help physicians to diagnose and to improve disease in the early stages |
| format | Article |
| id | doaj-art-abf9d3e1f08b4f4ea92aacd90b7d9c09 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2020-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-abf9d3e1f08b4f4ea92aacd90b7d9c092025-01-30T20:17:43ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752020-09-016216718110.22091/jemsc.2018.12771277Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector MachineZeinab Hassani0Mahin Khosravi1Engineering Faculty, Kosar University of Bojnord, Bojnurd, IranEngineering Faculty, Kosar University of Bojnord, Bojnurd, IranIn recent years, machine learning algorithms are widely used for diagnosis and timely treatment of diseases. Moreover, diagnosis of disease on early stages is very effective in improving the disease and in reducing the cost of treatment for the patient. Heart disease is one of the main causes of death in the world. Several studies have been conducted to diagnose of disease and to design an intelligent and efficient system. In this paper, a hybrid algorithm of Whale Optimization Algorithm and simulated annealing are presented to identify the effective factors in the diagnosis of the disease. The support vector machine algorithm is considered for effective classification of the disease. The proposed approach is evaluated using the Cleveland Heart Disease Data Collection in the UCI database. The proposed algorithm has obtained with an accuracy of 87.78% which is able to diagnose of disease with fewer attributes. The results exhibition the superiority of the proposed method which the proposed approach can help physicians to diagnose and to improve disease in the early stageshttps://jemsc.qom.ac.ir/article_1277_0034b961ec78c93b97ef0ba0714e9a8a.pdfcoronary heart diseasesupport vector machinewhale optimization algorithmsimulated annealing |
| spellingShingle | Zeinab Hassani Mahin Khosravi Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine مدیریت مهندسی و رایانش نرم coronary heart disease support vector machine whale optimization algorithm simulated annealing |
| title | Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine |
| title_full | Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine |
| title_fullStr | Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine |
| title_full_unstemmed | Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine |
| title_short | Diagnosis of Coronary Heart Disease by Using Hybrid Intelligent Systems Based on the Whale Optimization Algorithm, Simulated Annealing and Support Vector Machine |
| title_sort | diagnosis of coronary heart disease by using hybrid intelligent systems based on the whale optimization algorithm simulated annealing and support vector machine |
| topic | coronary heart disease support vector machine whale optimization algorithm simulated annealing |
| url | https://jemsc.qom.ac.ir/article_1277_0034b961ec78c93b97ef0ba0714e9a8a.pdf |
| work_keys_str_mv | AT zeinabhassani diagnosisofcoronaryheartdiseasebyusinghybridintelligentsystemsbasedonthewhaleoptimizationalgorithmsimulatedannealingandsupportvectormachine AT mahinkhosravi diagnosisofcoronaryheartdiseasebyusinghybridintelligentsystemsbasedonthewhaleoptimizationalgorithmsimulatedannealingandsupportvectormachine |