{"title":"改进冠状动脉疾病预测:使用随机森林、特征重要性和基于案例的推理","authors":"F. Henni, B. Atmani, F. Atmani, F. Saadi","doi":"10.4018/ijdsst.319307","DOIUrl":null,"url":null,"abstract":"Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.","PeriodicalId":42414,"journal":{"name":"International Journal of Decision Support System Technology","volume":"15 1","pages":"1-17"},"PeriodicalIF":0.6000,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving Coronary Artery Disease Prediction: Use of Random Forest, Feature Importance and Case-Based Reasoning\",\"authors\":\"F. Henni, B. Atmani, F. Atmani, F. Saadi\",\"doi\":\"10.4018/ijdsst.319307\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.\",\"PeriodicalId\":42414,\"journal\":{\"name\":\"International Journal of Decision Support System Technology\",\"volume\":\"15 1\",\"pages\":\"1-17\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Decision Support System Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijdsst.319307\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Decision Support System Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijdsst.319307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Improving Coronary Artery Disease Prediction: Use of Random Forest, Feature Importance and Case-Based Reasoning
Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.