HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm
Heart disease is the leading cause of mortality globally. Heart disease refers to a range of disorders that affect the heart and blood vessels. The risks of developing heart disease become minimized if heart disease is detected early. Previous studies have suggested many heart disease decision-suppo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2023-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3027 |
| _version_ | 1797810737591091200 |
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| author | M. Dhilsath Fathima S. Justin Samuel S. P. Raja |
| author_facet | M. Dhilsath Fathima S. Justin Samuel S. P. Raja |
| author_sort | M. Dhilsath Fathima |
| collection | DOAJ |
| description | Heart disease is the leading cause of mortality globally. Heart disease refers to a range of disorders that affect the heart and blood vessels. The risks of developing heart disease become minimized if heart disease is detected early. Previous studies have suggested many heart disease decision-support systems based on machine learning (ML) algorithms. However, the lower prediction accuracy is the main issue in these heart disease decisionsupport systems. The proposed work developed a heart disease decision-support system (HDDSS) that can predict whether or not a person has heart disease. The main goal of this research work is to use the RFEABGNB to improve HDDSS prediction accuracy. The Cleveland heart disease dataset is used for training and validating the proposed HDDSS. The two significant stages of HDDSS are the feature election stage and the classification modeling stage. The recursive feature elimination (RFE) technique is used in the first stage of HDDSS to select the relevant features of the heart disease dataset. In the second stage of HDDSS, the proposed Adaptive boosted Gaussian Naïve Bayes (ABGNB) algorithm has been used to construct a classification model for training and validating a heart disease decision-support system. An output of HDDSS is analyzed using various classification output measures. According to the results obtained, our proposed method attained a predictive performance of 92.87 percent. This HDDSS model would perform well when compared to other heart disease decision-support systems found in the literature. According to our experimental analysis, the RFE-ABGNB focused heart disease decision-support system is more appropriate for a heart disease prediction. |
| first_indexed | 2024-03-13T07:12:18Z |
| format | Article |
| id | doaj.art-d718114a06d14296b29c915cf29168bd |
| institution | Directory Open Access Journal |
| issn | 1989-1660 |
| language | English |
| last_indexed | 2024-03-13T07:12:18Z |
| publishDate | 2023-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-d718114a06d14296b29c915cf29168bd2023-06-05T20:31:39ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602023-06-0182293710.9781/ijimai.2021.10.003ijimai.2021.10.003HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB AlgorithmM. Dhilsath FathimaS. Justin SamuelS. P. RajaHeart disease is the leading cause of mortality globally. Heart disease refers to a range of disorders that affect the heart and blood vessels. The risks of developing heart disease become minimized if heart disease is detected early. Previous studies have suggested many heart disease decision-support systems based on machine learning (ML) algorithms. However, the lower prediction accuracy is the main issue in these heart disease decisionsupport systems. The proposed work developed a heart disease decision-support system (HDDSS) that can predict whether or not a person has heart disease. The main goal of this research work is to use the RFEABGNB to improve HDDSS prediction accuracy. The Cleveland heart disease dataset is used for training and validating the proposed HDDSS. The two significant stages of HDDSS are the feature election stage and the classification modeling stage. The recursive feature elimination (RFE) technique is used in the first stage of HDDSS to select the relevant features of the heart disease dataset. In the second stage of HDDSS, the proposed Adaptive boosted Gaussian Naïve Bayes (ABGNB) algorithm has been used to construct a classification model for training and validating a heart disease decision-support system. An output of HDDSS is analyzed using various classification output measures. According to the results obtained, our proposed method attained a predictive performance of 92.87 percent. This HDDSS model would perform well when compared to other heart disease decision-support systems found in the literature. According to our experimental analysis, the RFE-ABGNB focused heart disease decision-support system is more appropriate for a heart disease prediction.https://www.ijimai.org/journal/bibcite/reference/3027abgnb algorithmheart disease predictionmachine learningrecursive feature eliminationuci heart disease dataset |
| spellingShingle | M. Dhilsath Fathima S. Justin Samuel S. P. Raja HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm International Journal of Interactive Multimedia and Artificial Intelligence abgnb algorithm heart disease prediction machine learning recursive feature elimination uci heart disease dataset |
| title | HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm |
| title_full | HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm |
| title_fullStr | HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm |
| title_full_unstemmed | HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm |
| title_short | HDDSS: An Enhanced Heart Disease Decision Support System using RFE-ABGNB Algorithm |
| title_sort | hddss an enhanced heart disease decision support system using rfe abgnb algorithm |
| topic | abgnb algorithm heart disease prediction machine learning recursive feature elimination uci heart disease dataset |
| url | https://www.ijimai.org/journal/bibcite/reference/3027 |
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