Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease
Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors. First, the CVD problem reflects an urgent issue. A high...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/12/1406 |
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| author | You-Shyang Chen Ching-Hsue Cheng Su-Fen Chen Jhe-You Jhuang |
| author_facet | You-Shyang Chen Ching-Hsue Cheng Su-Fen Chen Jhe-You Jhuang |
| author_sort | You-Shyang Chen |
| collection | DOAJ |
| description | Since 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors. First, the CVD problem reflects an urgent issue. A high priority has been placed on long-term therapy and prevention to reduce the wastage of medical resources, particularly in developed countries. Second, from the perspective of preventive medicine, popular data-mining methods have been well learned and studied, with excellent performance in medical fields. Thus, identification of the risk factors of CVD using these popular techniques is a prime concern. Third, the Framingham risk score is a core indicator that can be used to establish an effective prediction model to accurately diagnose CVD. Thus, this study proposes an integrated predictive model to organize five notable classifiers: the rough set (RS), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM), with a novel use of the Framingham risk score for attribute selection (i.e., F-attributes first identified in this study) to determine the key features for identifying CVD. Verification experiments were conducted with three evaluation criteria—accuracy, sensitivity, and specificity—based on 1190 instances of a CVD dataset available from a Taiwan teaching hospital and 2019 examples from a public Framingham dataset. Given the empirical results, the SVM showed the best performance in terms of accuracy (99.67%), sensitivity (99.93%), and specificity (99.71%) in all F-attributes in the CVD dataset compared to the other listed classifiers. The RS showed the highest performance in terms of accuracy (85.11%), sensitivity (86.06%), and specificity (85.19%) in most of the F-attributes in the Framingham dataset. The above study results support novel evidence that no classifier or model is suitable for all practical datasets of medical applications. Thus, identifying an appropriate classifier to address specific medical data is important. Significantly, this study is novel in its calculation and identification of the use of key Framingham risk attributes integrated with the DT technique to produce entropy-based decision rules of knowledge sets, which has not been undertaken in previous research. This study conclusively yielded meaningful entropy-based knowledgeable rules in tree structures and contributed to the differentiation of classifiers from the two datasets with three useful research findings and three helpful management implications for subsequent medical research. In particular, these rules provide reasonable solutions to simplify processes of preventive medicine by standardizing the formats and codes used in medical data to address CVD problems. The specificity of these rules is thus significant compared to those of past research. |
| first_indexed | 2024-03-10T14:07:03Z |
| format | Article |
| id | doaj.art-978145e68f8249b0a7d4e1803bc7ae09 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T14:07:03Z |
| publishDate | 2020-12-01 |
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| spelling | doaj.art-978145e68f8249b0a7d4e1803bc7ae092023-11-21T00:35:39ZengMDPI AGEntropy1099-43002020-12-012212140610.3390/e22121406Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular DiseaseYou-Shyang Chen0Ching-Hsue Cheng1Su-Fen Chen2Jhe-You Jhuang3Department of Information Management, Hwa Hsia University of Technology, New Taipei City 235, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, TaiwanNational Museum of Marine Science & Technology, Keelung City 202010, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, TaiwanSince 2001, cardiovascular disease (CVD) has had the second-highest mortality rate, about 15,700 people per year, in Taiwan. It has thus imposed a substantial burden on medical resources. This study was triggered by the following three factors. First, the CVD problem reflects an urgent issue. A high priority has been placed on long-term therapy and prevention to reduce the wastage of medical resources, particularly in developed countries. Second, from the perspective of preventive medicine, popular data-mining methods have been well learned and studied, with excellent performance in medical fields. Thus, identification of the risk factors of CVD using these popular techniques is a prime concern. Third, the Framingham risk score is a core indicator that can be used to establish an effective prediction model to accurately diagnose CVD. Thus, this study proposes an integrated predictive model to organize five notable classifiers: the rough set (RS), decision tree (DT), random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM), with a novel use of the Framingham risk score for attribute selection (i.e., F-attributes first identified in this study) to determine the key features for identifying CVD. Verification experiments were conducted with three evaluation criteria—accuracy, sensitivity, and specificity—based on 1190 instances of a CVD dataset available from a Taiwan teaching hospital and 2019 examples from a public Framingham dataset. Given the empirical results, the SVM showed the best performance in terms of accuracy (99.67%), sensitivity (99.93%), and specificity (99.71%) in all F-attributes in the CVD dataset compared to the other listed classifiers. The RS showed the highest performance in terms of accuracy (85.11%), sensitivity (86.06%), and specificity (85.19%) in most of the F-attributes in the Framingham dataset. The above study results support novel evidence that no classifier or model is suitable for all practical datasets of medical applications. Thus, identifying an appropriate classifier to address specific medical data is important. Significantly, this study is novel in its calculation and identification of the use of key Framingham risk attributes integrated with the DT technique to produce entropy-based decision rules of knowledge sets, which has not been undertaken in previous research. This study conclusively yielded meaningful entropy-based knowledgeable rules in tree structures and contributed to the differentiation of classifiers from the two datasets with three useful research findings and three helpful management implications for subsequent medical research. In particular, these rules provide reasonable solutions to simplify processes of preventive medicine by standardizing the formats and codes used in medical data to address CVD problems. The specificity of these rules is thus significant compared to those of past research.https://www.mdpi.com/1099-4300/22/12/1406applications of medicinecardiovascular diseaseFramingham risk score (FRS)Framingham risk attributesentropy-based rule modelmachine learning techniques |
| spellingShingle | You-Shyang Chen Ching-Hsue Cheng Su-Fen Chen Jhe-You Jhuang Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease Entropy applications of medicine cardiovascular disease Framingham risk score (FRS) Framingham risk attributes entropy-based rule model machine learning techniques |
| title | Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease |
| title_full | Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease |
| title_fullStr | Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease |
| title_full_unstemmed | Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease |
| title_short | Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease |
| title_sort | identification of the framingham risk score by an entropy based rule model for cardiovascular disease |
| topic | applications of medicine cardiovascular disease Framingham risk score (FRS) Framingham risk attributes entropy-based rule model machine learning techniques |
| url | https://www.mdpi.com/1099-4300/22/12/1406 |
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