RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data

Amjad Rehman; Teg Alam; Muhammad Mujahid; Faten S Alamri; Bayan Al Ghofaily; Tanzila Saba

doi:10.7717/peerj-cs.1684

RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data

PeerJ Comput Sci. 2023 Nov 21:9:e1684. doi: 10.7717/peerj-cs.1684. eCollection 2023.

Authors

Amjad Rehman¹, Teg Alam^{2

3}, Muhammad Mujahid¹, Faten S Alamri⁴, Bayan Al Ghofaily¹, Tanzila Saba¹

Affiliations

¹ Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, Saudi Arabia.
² Department of Industrial Engineering, College of Engineering, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia.
³ Azad Institute of Engineering and Technology, Azad Puram, Chandrawal via Bangla Bazar & Bijnour, Near CRPF Camp, Lucknow, India.
⁴ Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Abstract

The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments and assistance. This early diagnosis plays a crucial role in managing symptoms effectively and enhancing the overall quality of life for individuals affected by the stroke. The research proposed an ensemble machine learning (ML) model that predicts brain stroke while reducing parameters and computational complexity. The dataset was obtained from an open-source website Kaggle and the total number of participants is 3,254. However, this dataset needs a significant class imbalance problem. To address this issue, we utilized Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADAYSN), a technique for oversampling issues. The primary focus of this study centers around developing a stacking and voting approach that exhibits exceptional performance. We propose a stacking ensemble classifier that is more accurate and effective in predicting stroke disease in order to improve the classifier's performance and minimize overfitting problems. To create a final stronger classifier, the study used three tree-based ML classifiers. Hyperparameters are used to train and fine-tune the random forest (RF), decision tree (DT), and extra tree classifier (ETC), after which they were combined using a stacking classifier and a k-fold cross-validation technique. The effectiveness of this method is verified through the utilization of metrics such as accuracy, precision, recall, and F1-score. In addition, we utilized nine ML classifiers with Hyper-parameter tuning to predict the stroke and compare the effectiveness of Proposed approach with these classifiers. The experimental outcomes demonstrated the superior performance of the stacking classification method compared to other approaches. The stacking method achieved a remarkable accuracy of 100% as well as exceptional F1-score, precision, and recall score. The proposed approach demonstrates a higher rate of accurate predictions compared to previous techniques.

Keywords: Machine learning; SMOTE; Stacking ensemble; Stroke prediction; Voting classifier.

Grants and funding

This research was funded by Princess Nourah bint Abdulrahman University and Researchers Supporting Project number (PNURSP2023R346), and Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The Prince Sultan University, Riyadh Saudi Arabia supported the APC of this publication. The funders had a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.