An improved hybrid model for cardiovascular disease detection using machine learning in IoT
Naseer, Arslan; Khan, Muhammad Muheet; Arif, Fahim; Iqbal, Waseem; Ahmad, Awais; Ahmad, Ijaz (2023-12-19)
Wiley-Blackwell
19.12.2023
Naseer, A., Khan, M. M., Arif, F., Iqbal, W., Ahmad, A., & Ahmad, I. (2025). An improved hybrid model for cardiovascular disease detection using machine learning in IoT. Expert Systems, 42(1), e13520. https://doi.org/10.1111/exsy.13520
https://rightsstatements.org/vocab/InC/1.0/
© 2023 John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Naseer, A., Khan, M. M., Arif, F., Iqbal, W., Ahmad, A., & Ahmad, I. (2023). An improved hybrid model for cardiovascular disease detection using machine learning in IoT. Expert Systems, e13520, which has been published in final form at https://doi.org/10.1111/exsy.13520. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
https://rightsstatements.org/vocab/InC/1.0/
© 2023 John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Naseer, A., Khan, M. M., Arif, F., Iqbal, W., Ahmad, A., & Ahmad, I. (2023). An improved hybrid model for cardiovascular disease detection using machine learning in IoT. Expert Systems, e13520, which has been published in final form at https://doi.org/10.1111/exsy.13520. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
https://rightsstatements.org/vocab/InC/1.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202410186383
https://urn.fi/URN:NBN:fi:oulu-202410186383
Tiivistelmä
Abstract
Cardiovascular disease (CVD) believes to be a major cause of transience and indisposition worldwide. Early diagnosis and timely intervention are critical in preventing the progression of CVD and improving patient outcomes. Machine learning (ML) algorithms have emerged as powerful tools in CVD recognition, with the potential to assist physicians in making accurate and efficient diagnoses. This research paper explores the combination of multiple ML algorithms for CVD recognition, utilizing diverse datasets such as the Cleveland, Hungarian, Switzerland, statlog, and VA Long Beach datasets. Additionally, a CVD dataset comprising 12 attributes and 70,000 records is employed, demonstrating improved results through the proposed and trained model compared to previous prediction techniques for CVD. The performance of various ML techniques, including support vector machines (SVM), naive Bayes (NB), K-nearest neighbour (KNN), random forest (RF), and logistic regression (LR), is evaluated and compared. The impact of feature selection and feature scaling on the models' performance is also examined. An ensemble bagging technique is applied which is being embedded with other classifiers. LR classifier embedded with bagging techniques proved to be our proposed model. The findings reveal that the proposed Hybrid Linear Regression Bagging Model (HLRBM) outperforms other models. Furthermore, the study highlights the significance of data preprocessing techniques, such as data normalization and class balancing, which significantly enhance the performance of all models. To this end, standard scalar and synthetic minority over-sampling technique (SMOTE) are employed. The study emphasizes the importance of selecting an appropriate ensemble technique in conjunction with various ML algorithms and preprocessing methods for CVD prediction. Overall, the research provides valuable insights into the potential of ML in improving CVD risk assessment.
Cardiovascular disease (CVD) believes to be a major cause of transience and indisposition worldwide. Early diagnosis and timely intervention are critical in preventing the progression of CVD and improving patient outcomes. Machine learning (ML) algorithms have emerged as powerful tools in CVD recognition, with the potential to assist physicians in making accurate and efficient diagnoses. This research paper explores the combination of multiple ML algorithms for CVD recognition, utilizing diverse datasets such as the Cleveland, Hungarian, Switzerland, statlog, and VA Long Beach datasets. Additionally, a CVD dataset comprising 12 attributes and 70,000 records is employed, demonstrating improved results through the proposed and trained model compared to previous prediction techniques for CVD. The performance of various ML techniques, including support vector machines (SVM), naive Bayes (NB), K-nearest neighbour (KNN), random forest (RF), and logistic regression (LR), is evaluated and compared. The impact of feature selection and feature scaling on the models' performance is also examined. An ensemble bagging technique is applied which is being embedded with other classifiers. LR classifier embedded with bagging techniques proved to be our proposed model. The findings reveal that the proposed Hybrid Linear Regression Bagging Model (HLRBM) outperforms other models. Furthermore, the study highlights the significance of data preprocessing techniques, such as data normalization and class balancing, which significantly enhance the performance of all models. To this end, standard scalar and synthetic minority over-sampling technique (SMOTE) are employed. The study emphasizes the importance of selecting an appropriate ensemble technique in conjunction with various ML algorithms and preprocessing methods for CVD prediction. Overall, the research provides valuable insights into the potential of ML in improving CVD risk assessment.
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