Optimizing Stroke Risk Prediction Using Symptom-Based Feature Selection
| dc.contributor.author | Bhuiyan, Refat Ahmed | |
| dc.contributor.author | Sarkar, Ahnaf Abid | |
| dc.contributor.author | Bisma, Tahya Ahammed | |
| dc.date.accessioned | 2025-02-28T05:41:28Z | |
| dc.date.available | 2025-02-28T05:41:28Z | |
| dc.date.issued | 2024-06-25 | |
| dc.description | Supervised by Mr. Ahmad Shafiullah, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024 | |
| dc.description.abstract | Stroke is a significant health concern, with early detection being challenging. Our research employs symptom-based feature selection using chi-square analysis and RFECV. By applying a logistic regression algorithm, we achieved 93% accuracy with just 9 features. | |
| dc.identifier.citation | [1] E. S. Donkor, ‘Stroke in the 21st century: a snapshot of the burden, epidemiology, and quality of life’, Stroke Res Treat, vol. 2018, no. 1, p. 3238165, 2018. [2] A. Alloubani, A. Saleh, and I. Abdelhafiz, ‘Hypertension and diabetes mellitus as a predictive risk factors for stroke’, Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 12, no. 4, pp. 577–584, 2018. [3] S. Dev, H. Wang, C. S. Nwosu, N. Jain, B. Veeravalli, and D. John, ‘A predictive analytics approach for stroke prediction using machine learning and neural networks’, Healthcare Analytics, vol. 2, p. 100032, 2022. [4] M. U. Emon, M. S. Keya, T. I. Meghla, M. M. Rahman, M. S. A. Mamun, and M. S. Kaiser, ‘Performance Analysis of Machine Learning Approaches in Stroke Prediction’, in 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 1464–1469. doi: 10.1109/ICECA49313.2020.9297525. [5] V. Bandi, D. Bhattacharyya, and D. Midhunchakkravarthy, ‘Prediction of Brain Stroke Severity Using Machine Learning.’, Rev. d’Intelligence Artif., vol. 34, no. 6, pp. 753–761, 2020. [6] G. Sailasya and G. L. A. Kumari, ‘Analyzing the performance of stroke prediction using ML classification algorithms’, International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, 2021. [7] N. Biswas, K. M. M. Uddin, S. T. Rikta, and S. K. Dey, ‘A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach’, Healthcare Analytics, vol. 2, p. 100116, 2022. [8] R. Islam, S. Debnath, and T. I. Palash, ‘Predictive Analysis for Risk of Stroke Using Machine Learning Techniques’, in 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), 2021, pp. 1–4. doi: 10.1109/IC4ME253898.2021.9768524. [9] A. Khosla, Y. Cao, C. C.-Y. Lin, H.-K. Chiu, J. Hu, and H. Lee, ‘An integrated machine learning approach to stroke prediction’, in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 183–192. [10] I. Mosley, M. Nicol, G. Donnan, I. Patrick, and H. Dewey, ‘Stroke symptoms and the decision to call for an ambulance’, Stroke, vol. 38, no. 2, pp. 361–366, 2007. [11] N. Akhtar et al., ‘Stroke Mimics: A five-year follow-up study from the Qatar Stroke Database’, Journal of Stroke and Cerebrovascular Diseases, vol. 29, no. 10, p. 105110, 2020, doi: https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105110. [12] C. T. Kim, ‘Stroke rehabilitation’, in Rehabilitation Medicine, IntechOpen, 2012. [13] W. G. Mantyh, A. Chandregowda, J. R. Fulgham, K. D. Flemming, R. S. Laughlin, and D. T. Jones, ‘Teaching Video NeuroImages: Foix-Chavany-Marie syndrome’, Neurology, vol. 92, no. 22, pp. e2620– e2621, 2019. [14] K. M. Sand, A. Midelfart, L. Thomassen, A. Melms, H. Wilhelm, and J. M. Hoff, ‘Visual impairment in stroke patients–a review’, Acta Neurol Scand, vol. 127, pp. 52–56, 2013. [15] T. Al Bahri, S. M. Bell, A. Majid, and J. Janbieh, ‘Dominant parietal lobe ischaemic stroke presenting as alien hand syndrome’, ACNR, Advances in Clinical Neuroscience and Rehabilitation, 1922. [16] S. L. Demel, S. Kittner, S. H. Ley, M. McDermott, and K. M. Rexrode, ‘Stroke risk factors unique to women’, Stroke, vol. 49, no. 3, pp. 518–523, 2018. [17] N. Pandis, ‘The chi-square test’, American journal of orthodontics and dentofacial orthopedics, vol. 150, no. 5, pp. 898–899, 2016. [18] H. Liang, J. Wu, H. Zhang, and J. Yang, ‘Two-stage short-term power load forecasting based on RFECV feature selection algorithm and a TCN–ECA–LSTM neural network’, Energies (Basel), vol. 16, no. 4, p. 1925, 2023. [19] L. Breiman, ‘Random forests’, Mach Learn, vol. 45, pp. 5–32, 2001. [20] J. Xu, Y. Zhang, and D. Miao, ‘Three-way confusion matrix for classification: A measure driven view’, Inf Sci (N Y), vol. 507, pp. 772–794, 2020. [21] D. J. Hand, ‘Measuring classifier performance: a coherent alternative to the area under the ROC curve’, Mach Learn, vol. 77, no. 1, pp. 103–123, 2009 | |
| dc.identifier.other | https://repository.iutoic-dhaka.edu/server/api/core/items/b239e8ee-af42-4313-96ac-5cc07f132883 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/2327 | |
| dc.language.iso | en | |
| dc.publisher | Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh | |
| dc.source | IUT Institutional Repository | |
| dc.subject | Stroke, Machine Learning,Symptoms,Chi-Square,RFECV, | |
| dc.title | Optimizing Stroke Risk Prediction Using Symptom-Based Feature Selection | |
| dc.type | Thesis |
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