A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques
Abstract Aims The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. Methods In this paper, we have utilized machine learning algorithms to pred...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | ESC Heart Failure |
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| Online Access: | https://doi.org/10.1002/ehf2.14942 |
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| author | Azka Mir Attique Ur Rehman Tahir Muhammad Ali Sabeen Javaid Maram Fahaad Almufareh Mamoona Humayun Momina Shaheen |
| author_facet | Azka Mir Attique Ur Rehman Tahir Muhammad Ali Sabeen Javaid Maram Fahaad Almufareh Mamoona Humayun Momina Shaheen |
| author_sort | Azka Mir |
| collection | DOAJ |
| description | Abstract Aims The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. Methods In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy. Results The proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively. Conclusions In conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages. |
| format | Article |
| id | doaj-art-e013c77d0fe54af28705fc05575738c2 |
| institution | DOAJ |
| issn | 2055-5822 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | ESC Heart Failure |
| spelling | doaj-art-e013c77d0fe54af28705fc05575738c22025-08-20T02:48:49ZengWileyESC Heart Failure2055-58222024-12-011163742375610.1002/ehf2.14942A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniquesAzka Mir0Attique Ur Rehman1Tahir Muhammad Ali2Sabeen Javaid3Maram Fahaad Almufareh4Mamoona Humayun5Momina Shaheen6Department of Software Engineering University of Sialkot Sialkot PakistanDepartment of Software Engineering University of Sialkot Sialkot PakistanDepartment of Computer Science Gulf University for Sciences and Technology Hawally KuwaitDepartment of Software Engineering University of Sialkot Sialkot PakistanDepartment of Information Systems College of Computer and Information Science Jouf University Sakaka Saudi ArabiaDepartment of Information Systems College of Computer and Information Science Jouf University Sakaka Saudi ArabiaSchool of Arts Humanities and Social Sciences University of Roehampton London UKAbstract Aims The objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease. Methods In this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy. Results The proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively. Conclusions In conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages.https://doi.org/10.1002/ehf2.14942cardiovascular disease predictiondata imbalance handlingmulti‐dataset approachhealthcare applicationsCVD prediction using machine learning |
| spellingShingle | Azka Mir Attique Ur Rehman Tahir Muhammad Ali Sabeen Javaid Maram Fahaad Almufareh Mamoona Humayun Momina Shaheen A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques ESC Heart Failure cardiovascular disease prediction data imbalance handling multi‐dataset approach healthcare applications CVD prediction using machine learning |
| title | A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques |
| title_full | A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques |
| title_fullStr | A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques |
| title_full_unstemmed | A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques |
| title_short | A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques |
| title_sort | novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques |
| topic | cardiovascular disease prediction data imbalance handling multi‐dataset approach healthcare applications CVD prediction using machine learning |
| url | https://doi.org/10.1002/ehf2.14942 |
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