Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review
Abstract Introduction Several medical decision support systems for heart disease prediction have been developed by different researchers in today's digital and artificial intelligence-driven society to simplify and ensure effective diagnosis by utilising machine learning (ML) algorithms. Purpos...
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Discover Artificial Intelligence |
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| Online Access: | https://doi.org/10.1007/s44163-024-00181-w |
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| author | Francis Adoba Ekle Vincent Shidali Richard Emoche Ochogwu Igoche Bernard Igoche |
| author_facet | Francis Adoba Ekle Vincent Shidali Richard Emoche Ochogwu Igoche Bernard Igoche |
| author_sort | Francis Adoba Ekle |
| collection | DOAJ |
| description | Abstract Introduction Several medical decision support systems for heart disease prediction have been developed by different researchers in today's digital and artificial intelligence-driven society to simplify and ensure effective diagnosis by utilising machine learning (ML) algorithms. Purpose To carry out a systematic comparative review of the performance of variant supervised learning ML models for heart disease prediction, and also propose a Dietary Approach to Stop Hypertension (DASH) lifestyle change therapy recommendation system blueprint for heart disease. Methods In this research, the authors sourced 61 articles that used more than one supervised learning ML algorithms on heart disease prediction for comparison from Google Scholar and PubMed databases. A content-based filtering recommendation technique was used for designing the proposed system blueprint. Results Comparatively, the Voting Ensembles Classifier (VEC) algorithm demonstrated the highest accuracy. This is hinged on the fact that, although each model may slightly overfit or underfit the data, their errors can cancel out when used in combination to produce predictions that are more accurate and stable. Furthermore, VEC's more reliable predictions can improve healthcare management's overall efficiency. Lastly, this study showed the blueprint of the proposed dietary therapy recommendation system for heart disease. Conclusion This research offers an extensive summary of the comparative performance of different supervised learning ML algorithms for heart disease prediction and also proposes a dietary lifestyle change therapy recommendation system framework. The information on comparative performance can aid researchers in choosing a suitable ML algorithm for their research, and the proposed system can act as a dietary therapy support tool for cardiologists when fully implemented. |
| format | Article |
| id | doaj-art-9ac2fab9bf3c44838139cb4bbeef2d15 |
| institution | DOAJ |
| issn | 2731-0809 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Artificial Intelligence |
| spelling | doaj-art-9ac2fab9bf3c44838139cb4bbeef2d152025-08-20T02:39:41ZengSpringerDiscover Artificial Intelligence2731-08092024-12-014112010.1007/s44163-024-00181-wMachine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic reviewFrancis Adoba Ekle0Vincent Shidali1Richard Emoche Ochogwu2Igoche Bernard Igoche3Department of Computer Science, University of NigeriaPhysician Consultant Cardiologist in the Department of Internal Medicine, Federal Medical CentreDoctoral Student in the Department of Computer Science, University of NigeriaDoctoral Student in the School of Computing, University of PortsmouthAbstract Introduction Several medical decision support systems for heart disease prediction have been developed by different researchers in today's digital and artificial intelligence-driven society to simplify and ensure effective diagnosis by utilising machine learning (ML) algorithms. Purpose To carry out a systematic comparative review of the performance of variant supervised learning ML models for heart disease prediction, and also propose a Dietary Approach to Stop Hypertension (DASH) lifestyle change therapy recommendation system blueprint for heart disease. Methods In this research, the authors sourced 61 articles that used more than one supervised learning ML algorithms on heart disease prediction for comparison from Google Scholar and PubMed databases. A content-based filtering recommendation technique was used for designing the proposed system blueprint. Results Comparatively, the Voting Ensembles Classifier (VEC) algorithm demonstrated the highest accuracy. This is hinged on the fact that, although each model may slightly overfit or underfit the data, their errors can cancel out when used in combination to produce predictions that are more accurate and stable. Furthermore, VEC's more reliable predictions can improve healthcare management's overall efficiency. Lastly, this study showed the blueprint of the proposed dietary therapy recommendation system for heart disease. Conclusion This research offers an extensive summary of the comparative performance of different supervised learning ML algorithms for heart disease prediction and also proposes a dietary lifestyle change therapy recommendation system framework. The information on comparative performance can aid researchers in choosing a suitable ML algorithm for their research, and the proposed system can act as a dietary therapy support tool for cardiologists when fully implemented.https://doi.org/10.1007/s44163-024-00181-wMachine learningSupervised learning machine learningDASHHeart disease predictionDietary therapy |
| spellingShingle | Francis Adoba Ekle Vincent Shidali Richard Emoche Ochogwu Igoche Bernard Igoche Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review Discover Artificial Intelligence Machine learning Supervised learning machine learning DASH Heart disease prediction Dietary therapy |
| title | Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review |
| title_full | Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review |
| title_fullStr | Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review |
| title_full_unstemmed | Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review |
| title_short | Machine Learning models for heart disease prediction and dietary lifestyle change therapy recommendation: a systematic review |
| title_sort | machine learning models for heart disease prediction and dietary lifestyle change therapy recommendation a systematic review |
| topic | Machine learning Supervised learning machine learning DASH Heart disease prediction Dietary therapy |
| url | https://doi.org/10.1007/s44163-024-00181-w |
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