Rahimi Fatemeh, Nasiri Mahdi, Safdari Reza, Arji Goli, Hashemi Zahra, Sharifian Roxana
Department of Health, Information Management, School of Management and Medical Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Int J Prev Med. 2022 Dec 26;13:158. doi: 10.4103/ijpvm.IJPVM_504_20. eCollection 2022.
According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms.
The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14.2) software on the prepared data; and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software.
This study was able to predict MI % 75.28 and 77.77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively.
Predicting studies aim to support rather than replace clinical judgment. Our prediction models are not sufficiently accurate to supplant decision-making by physicians but have considerable tips about MI risk factors.
根据世界卫生组织（WHO）的数据，心血管疾病（CVDs）是全球主要的死亡原因。尽管在心血管疾病的诊断方面已经取得了显著进展，但更多的研究仍可能有所帮助。因此，本研究旨在使用数据挖掘算法预测心肌梗死（MI）的风险。
所应用的数据与位于德黑兰的拉贾伊心血管专科医院收治的患者有关。首先，进行了文献综述并与心脏病专家进行了访谈以了解心肌梗死。然后，进行数据准备（清理和规范化数据）。最后，在IBM SPSS Modeler（14.2）软件中对准备好的数据应用不同的分类算法；并且，在上述软件中计算所应用算法的效能以及风险因素在预测发生心肌梗死概率方面的重要性。
本研究在准确性和敏感性方面分别能够预测心肌梗死的概率为75.28%和77.77%。结果还显示，吸烟、成瘾、血压和胆固醇分别是预测发生心肌梗死概率的最重要风险因素。
预测性研究旨在提供支持而非取代临床判断。我们的预测模型准确性不足以取代医生的决策，但对心肌梗死的风险因素有相当多的提示。
