Abstract
Accurate classification of cardiovascular diseases (CVDs) is of utmost importance for cardiologists to provide appropriate treatments. Diagnosing and predicting cardiovascular conditions are crucial medical responsibilities in this context. The healthcare sector is increasingly utilizing deep learning (DL) and machine learning (ML) algorithms due to their ability to identify patterns in data. Diagnosticians may reduce the number of misdiagnoses by using DL and ML techniques for the categorization of cardiovascular disease incidence. To reduce the mortality linked to CVDs, this research offers a unique model that properly predicts and classifies these problems. This research presents approaches such as deep learning, random forest (RF), support vector machines (SVM), and K-nearest neighbors for predicting heart disease arrest. We implemented the suggested model using 303 real-world instances from Kaggle. In the testing stage, the KNN model's accuracy was 92%. By comparison, the accuracy of the DL model was 87%. The RF model's accuracy was 84%. The results indicate that the KNN algorithm outperforms other algorithms in terms of accuracy. We compared the study's results with a variety of existing systems.
Reason for Expression of Concern:The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in ``Improving Heart Attack Prediction Accuracy Performance Using Machine Learning and Deep Learning Algorithms (Manuscript 1239)''. The journal has initiated an additional editorial assessment of the article's methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility.
This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal's policies and COPE guidance. See expression of concern available at:
DOI: https://doi.org/10.52866/2788-7421.1383.
Available at: http://ijcsm.researchcommons.org/ijcsm/vol7/iss1/37
Recommended Citation
Al-Adhaileh, Mosleh Hmoud; Ahmed Al-mashhadani, Mohammed Ibrahim; Alzahrani, Eidah M; and Aldhyani, Theyazn H.H.
(2025)
"Improving Heart Attack Prediction Accuracy Performance Using Machine Learning and Deep Learning Algorithms,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
2, Article 3.
DOI: https://doi.org/10.52866/2788-7421.1239
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/3
