Volume 10, Issue 4 (3-2024)
jhbmi 2024, 10(4): 346-356 |
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Hosseinpoor M. Presenting an Intelligent Algorithm to Predict Heart Attack. jhbmi 2024; 10 (4) :346-356
URL: http://jhbmi.ir/article-1-763-en.html
URL: http://jhbmi.ir/article-1-763-en.html
Member of Faculty and Assistant Professor in Computer Engineering Department, Islamic Azad University, Estahban Branch, Estahban, Iran
Abstract: (152 Views)
Introduction: Today, heart attack is one of the most common causes of death in adults all over the world. According to the announcement of the Ministry of Health, Treatment, and Medical Education, 11 to 15 percent of deaths in Iran are caused by heart attacks, and in the world, Iran has the highest number of deaths due to heart disease in 2022. It has been estimated that deaths from these diseases will increase to 20 million people. Therefore, predicting this disease is one of the most challenging topics in the medical field, and today most prediction systems are created using artificial intelligence and machine learning algorithms.
Method: This applied and descriptive research was conducted in 2022 using the information of 600 people who referred to Peymaniyeh and Motahari hospitals in Jahrom city. In this research, data were collected based on the available sources from both hospitals. To implement and evaluate the results, MATLAB programming language was used.
Results: The parameters used in this research are among the classification parameters, including classification rate, precision, recall, and F-measure, which were obtained as 90.7, 90.2, 91.5, and 90.8, respectively.
Conclusion: The results show that the proposed model will be able to predict the probability of stroke with a high percentage.
Method: This applied and descriptive research was conducted in 2022 using the information of 600 people who referred to Peymaniyeh and Motahari hospitals in Jahrom city. In this research, data were collected based on the available sources from both hospitals. To implement and evaluate the results, MATLAB programming language was used.
Results: The parameters used in this research are among the classification parameters, including classification rate, precision, recall, and F-measure, which were obtained as 90.7, 90.2, 91.5, and 90.8, respectively.
Conclusion: The results show that the proposed model will be able to predict the probability of stroke with a high percentage.
Keywords: Heart attack, Disease prediction, Intelligent model, Machine learning, Support vector machine
Type of Study: Original Article |
Subject:
Artificial Intelligence in Healthcare
Received: 2023/03/4 | Accepted: 2024/02/12
Received: 2023/03/4 | Accepted: 2024/02/12
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