loading page

An Improved Hybrid Model for Cardiovascular Disease Detection Using Machine Learning in IoT
  • +2
  • Waseem Iqbal,
  • Arslan Naseer,
  • Muhammad Muheet Khan,
  • Fahim Arif,
  • Awais Ahmad
Waseem Iqbal
National University of Sciences and Technology Department of Information Security

Corresponding Author:waseem.iqbal@mcs.edu.pk

Author Profile
Arslan Naseer
National University of Sciences and Technology Department of Information Security
Author Profile
Muhammad Muheet Khan
National University of Sciences and Technology Department of Information Security
Author Profile
Fahim Arif
National University of Sciences and Technology Department of Information Security
Author Profile
Awais Ahmad
Imam Muhammad bin Saud Islamic University College of Computer and Information Science
Author Profile

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 Neighbor (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 techniques 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.
30 Aug 2023Submitted to Expert Systems
01 Sep 2023Submission Checks Completed
01 Sep 2023Assigned to Editor
18 Sep 2023Reviewer(s) Assigned
04 Oct 2023Review(s) Completed, Editorial Evaluation Pending
09 Oct 2023Editorial Decision: Revise Major
15 Oct 20231st Revision Received
16 Oct 2023Submission Checks Completed
16 Oct 2023Assigned to Editor
18 Oct 2023Reviewer(s) Assigned
30 Oct 2023Review(s) Completed, Editorial Evaluation Pending
04 Nov 2023Editorial Decision: Revise Minor
07 Nov 20232nd Revision Received
14 Nov 2023Submission Checks Completed
14 Nov 2023Assigned to Editor
19 Nov 2023Reviewer(s) Assigned