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Original Article

Ensemble Machine Learning for Functional Outcome Prognostication in Acute Ischemic Stroke: A Multiclass Classification Study Using Clinical Risk Factors

  • Paper ID 4
  • Volume 1, Issue 1
  • Pages: 08-12
  • Published Online: January-April 2026
  • 188 Views
Open Access Peer Reviewed

Authors

  • Dr Priyanka Shridharan1

1 Department of Biochemistry, BGS MCH College/ Adichunchanagiri University, Karnataka India.

Article Information

DOI
No Doi
Published Online
January-April 2026
Pages
08-12
Article Type
Original Article
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References

1. Viviani S. Thrombolysis with Alteplase 3 to 4.5 Hours after Acute Ischemic Stroke. N Engl J Med. 2011;365:687–696.
2. Bluhmki E, et al. Stroke treatment with alteplase given 3.0–4.5 h after onset of acute ischaemic stroke (ECASS III). Lancet Neurol.
2009;8(12):1095–1102.
3. Powers WJ, et al. 2018 Guidelines for the Early Management of Patients With Acute Ischemic Stroke. Stroke. 2018;49(3).
doi:10.1161/STR.0000000000000158
4. Nogueira RG, et al. Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct. N Engl J Med. 2018;378(1):11–
21.
5. Campbell BCV, et al. Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection. N Engl J Med. 2015;372(11):1009–1018.
6. Ni Y, et al. Towards phenotyping stroke: Leveraging data from a large-scale epidemiological study to detect stroke diagnosis. PLoS One.
2018;13(2):e0192586.
7. Tanioka S, et al. Machine Learning Analysis of Matricellular Proteins for Early Prediction of Delayed Cerebral Ischemia. Mol Neurobiol.
2019;56(10):7128–7135.
8. Kamal H, Lopez V, Sheth SA. Machine learning in acute ischemic stroke neuroimaging. Front Neurol. 2018;9:945.
9. Mainali S, Darsie ME, Smetana KS. Machine Learning in Action: Stroke Diagnosis and Outcome Prediction. Front Neurol. 2021;12:734345.
10. Tazin T, et al. Stroke Disease Detection and Prediction Using Robust Learning Approaches. J Healthc Eng. 2021;2021:7633381.11. Gangil T, et al. Predicting clinical outcomes of radiotherapy for head and neck squamous cell carcinoma using machine learning. J Big Data.
2022;9:36.
12. Chen T, He T. XGBoost: Extreme Gradient Boosting. R Lecture Notes. 2014;1–84.
13. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997;30(7):1145–
1159.
14. Probst P, Boulesteix AL, Bischl B. Tunability: Importance of hyperparameters of machine learning algorithms. J Mach Learn Res. 2019;20:1–
32.
15. Lalkhen AG, McCluskey A. Clinical tests: Sensitivity and specificity. Contin Educ Anaesthesia Crit Care Pain. 2008;8(6):221–223.
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