Authors : Md. Jahirul Islam; Sukhdeb Chandra Das; Md.Golam Mostofa

Volume/Issue : Volume 10 - 2025, Issue 8 - August

Google Scholar : https://tinyurl.com/e6hn2ku8

Scribd : https://tinyurl.com/yc82u9rh

DOI : https://doi.org/10.38124/ijisrt/25aug1562

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Note : Google Scholar may take 30 to 40 days to display the article.

Abstract : Stroke is currently one of the leading causes of mortality worldwide. Extensive research has identified several risk factors associated with stroke, which have been extensively studied to enhance prediction and categorization of the disease. Machine learning (ML) has proven to be a powerful tool for analyzing vast amounts of data, enabling accurate predictions and informed decision-making. Researchers are actively working to develop automated ML models for stroke prediction with the aim of facilitating early interventions and saving lives. As the global population ages and the number of individuals at risk for stroke continues to rise, the need for accurate prediction algorithms has become increasingly critical. The widespread adoption of Android applications provides an opportunity to make predictive tools accessible to a broader audience. This study investigates the application of ML algorithms in stroke risk prediction and demonstrates their integration into a functional Android application. The dataset used for model development was sourced from Kaggle, where a significant imbalance was observed with a ratio of 19:1 between instances of no stroke and stroke. To address this imbalance, Synthetic Minority Oversampling Technique (SMOTE) analysis was employed, ensuring a balanced dataset for training the models. Eight robust ML algorithms were utilized to develop predictive models. Among these, the Ensemble method leveraging a voting classifier achieved the best performance, attaining an accuracy of 95% and a recall of 95%. TensorFlow was used to integrate the machine learning model into an Android application, enabling real-time predictions. The Android app, developed in Java and built using the Android Studio platform, is designed to offer recommendations for stroke prevention and management. This user-friendly application aims to enhance accessibility to stroke risk predictions, empowering users to take proactive measures to safeguard their health..

Keywords : SMOTE Analysis, Android App, Machine Learning, Stroke, Recommendations, Android Studio.

References :

1. "Learn about Stroke. Available on: “[Online]. Available: https://www.world-stroke.org/world-stroke-day-campaign/why- stroke-matters/learnabout-stroke, [Accessed 2023].
2. T. Elloker and A. Rhoda, "The relationship between social support and participation in stroke: A systematic review.," pp.1-9, 2018.
3. M. Katan and A. Luft, "Global burden of stroke," in In seminars in Neurology, New work, USA, 2018.
4. X. Xia, W. Yue, B. Chao, M. Li, L. Cao, L. Wang, Y. Shen and X. Li, "Prevalence and risk factors of stroke in the elderly in Northern China: Data from the National Stroke Screening Survey. J. Neurol," pp. 1-12, 2019.
5. J. Lecouturier, M. Murtagh, R. Thomson, G. Ford, M. White, M. Eccles and H. Rodgers, "Response to symptoms of stroke in  the UK: A systematic review," pp. 1-9, 2010.
6. A. J. M. M. e. a. Aminul Haque, "AI Powered Asthma Prediction Towards Treatment Formulation: An Android App Approach," Intelligent Automation & Soft Computing (IASC), vol. 34, no. 1, pp. 1-18, 2022.
7. S. G. e. a. KRISHNA MRIDHA, "Automated Stroke Prediction Using Machine Learning: An Explainable and Exploratory Study with  a Web Application for Early Intervention," IEEE Access, vol. 11, pp. 1-24, 2023.
8. H. I. M. R. H. a. M. K. H. M. M. Islam, "“Prediction of breast cancer using support vector machine and K-Nearest neighbors," in 2017 IEEE Region 10 Humanitarian Technology Conf. (R10-HTC), Dhaka, Bangladesh, pp. 226-229, 2017.
9. M. R. H. H. I. M. M. H. M. H. e. a. M. M. Islam, "Breast cancer prediction: A comparative study using machine learning techniques," SN Computer Science, vol. 1, no. 5, p. 290, 2020.
10. M. M. I. a. M. M. A. H. M. K. Hasan, "Mathematical model development to detect breast cancer using multigene genetic programming,"in 5th Int. Conf. on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, pp. 574-557, 2016.
11. S. I. A. a. M. M. Islam, "Diabetes prediction: A deep learning approach," International Journal of Information  Engineering and Electronic Business (IJIEEB), vol. 11, no. 2, pp. 21-27, 2019.
12. S. R. Saumya Gupta, "Stroke Prediction using Machine Learning Methods,"12th International Conference on Cloud Computing, Data Science & Engineering (IEEE Xplore), pp. 1-6, 2022.
13. R. D. Viswa Priya S E, "A Systematic Method of Stroke Prediction Model based on Big Data and Machine Learning," IEEE  Access, pp. 1-6, 2023.
14. T. I., S. J. S. A. T. M. e. a. Tasfia Ismail Shoily, "Detection of Stroke Disease using Machine Learning Algorithms," IEEE Access, pp. 1-6, 2019.
15. H. W. e. a. Soumyabrata Dev, "A predictive analytics approach for stroke prediction using machine learning and neural networks," ELSEVIER, vol. 2, pp. 1-7, 2022.
16. M. H. I. R. M. P. S. a. H. M. Islam, "Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal. Sensors," MDPI, 2022, 22(24), 9859.
17. E. a. T. M. Dritsas, "Stroke risk prediction with machine learning techniques," Sensors, 2022, 22(13), p.4670.
18. R. D. S. a. P. T. Islam, "Predictive Analysis for Risk of Stroke Using Machine Learning Techniques," In 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2) (pp. 1-4). IEEE.
19. J. W. C. Youngkeun Choi, "Stroke Prediction Using Machine Learning based on Artificial Intelligence," International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE), Volume 9, No.5, September - October 2020.
20. T. A. M. D. N. e. a. Tazin, "Stroke Disease Detection and Prediction Using Robust Learning Approaches," Journal of  Healthcare Engineering, 2021.
21. H. P. G. G. V. P. a. P. K. B. H. K V, "STROKE PREDICTION USING MACHINE LEARNING ALGORITHMS," International Journal of Innovative Research in Engineering & Management, vol. 8, no. 4, Jul. 2021, Doi: 10.21276/ijirem.2021.8.4.2.
22. E. a. Ali, "Stroke prediction using distributed machine learning based on Apache spark," Stroke, 28(15), pp.89-97.
23. S.-M. H. e. al., "Stroke risk prediction through non-linear support vector classification models," Int. J. Adv. Res. Computer. Sci. 1 (3) (2010).
24. On, Kaggle, [Online]. Available: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset, [Accessed 2023].
25. Z. R. M. A. Z. H. H. N. J. P. a. M. K. S. A. Murad, "Computer-aided system for extending the performance of diabetes  analysis and prediction," in 7th Int. Conf. on Software Engineering and Computer Systems (ICSECS-2021), Pahang, Malaysia, pp. 465–470, 2021.
26. R. N. H. T. T. I. H. S. e. a. K. Tomita, "Deep learning facilitates the diagnosis of adult asthma," Allergology  International, vol. 68, no. 4, pp. 456–461, 2019.
27. M. P. K. R. G. V. M. R. a. K. M. M. P. B. P. Reddy, "Fake data analysis and detection using ensembled hybrid algorithm," in 3rd Int. Conf. on Computing Methodologies and Communication (ICCMC), Erode, India, pp. 890–897, 2019.
28. Cherif and A. Kortebi, "On using Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic  Classification," IEEE, vol. II, pp. 1-6, 2019.
29. D. K. P. a. M. P. A. Gupta, "Development of mobile application for laundry services using android studio," International  Journal of Applied Engineering Research, India, vol. 13, no. 12, pp. 10623–10626, 2018.
30. Abhi Android, "Android App Development Tutorial: Beginners Guide with Examples, Code and Tutorials," India. [Online]. Available: https://abhiandroid.com/java/. [Accessed 2023].
31. N. S. D. K. D. J. S. S. S. e. a. G. S. Bhat, "Machine learning-based asthma risk prediction using iot and smartphone  applications," IEEE Access, vol. 9, pp. 118708– 118715, 2021.
32. N. Y. a. J. E. DeBello, "Recommended practices for python pedagogy in graduate data science courses," IEEE Frontiers in Education Conf. (FIE), Nebraska, Lincoln, USA, pp. 1-7, 2020.
33. C. G. G. G. E. M. S. e. a. Kokkotis, "An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data," 2022, Diagnostics, 12(10), p.2392.