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Title: :  NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH
PaperId: :  26029
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26029
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Omprasad Narkhede Student, Computer Department, Sandip institute of engineering and management, MH, India
Sujal GodseStudent, Computer Department, Sandip institute of engineering and management, MH, India
Gavande Sudev Ankush Student, Computer Department, Sandip institute of engineering and management, MH, India
Akash KakadStudent, Computer Department, Sandip institute of engineering and management, MH, India
Prof. Harshal KumarProfessor , Computer Department, Sandip institute of engineering and management, MH, India

Abstract

Technology
Stroke Identification, Machine Learning,Neuroimages,Diagnostic Model,Logistic Regression,Support Vector Machine (SVM),Random Forest,Decision Tree,Convolutional Neural Network (CNN)
Stroke diagnosis is a time-critical process that requires rapid and accurate identification to ensure timely treatment.This study proposes a machine learning-based diagnostic model for stroke identification using neuroimages.We employed a comprehensive approach, utilizing logistic regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Network (CNN) algorithms to analyze neuroimages and predict stroke occurrence. Our model was trained and validated on a dataset of brain images, demonstrating exceptional performance in distinguishing between stroke and non-stroke cases.This abstract highlights the innovative approach of utilizing machine learning algorithms for stroke identification through neuroimages. The study proposes a diagnostic model that incorporates logistic regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Convolutional Neural Network (CNN) algorithms to accurately detect strokes in patients based on neuroimage data.The utilization of logistic regression allows for the analysis of relationships between neuroimage features and stroke presence, while SVM can effectively classify different patterns within the data. Random Forest and Decision Tree algorithms provide a structured framework for decision-making based on key image attributes, enabling accurate identification of stroke-related patterns. The integration of CNN algorithm further enhances the diagnostic precision by extracting relevant features from complex image structures.This multidimensional approach demonstrates promising potential in improving stroke identification processes through sophisticated machine learning techniques applied to neuroimaging data analysis.The results show that the CNN algorithm outperformed other models, achieving an accuracy of 95.6%, sensitivity of 94.2%, and specificity of 96.5%. The Random Forest and SVM models also demonstrated promising results, with accuracies of 93.1% and 92.5%, respectively. Logistic regression and Decision Tree models showed lower but still respectable performance. This study highlights the potential of machine learning-based approaches in improving stroke diagnosis, enabling healthcare professionals to make informed decisions and providing a valuable tool for stroke identification. Our model has the potential to enhance patient outcomes and reduce the economic burden of stroke.By leveraging the power of these advanced machine learning techniques, the model aims to enhance the efficiency and accuracy of stroke diagnosis compared to traditional methods.

Citations

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IJARIIE Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. "NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 824-832
MLA Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. "NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 824-832.
APA Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, & Prof. Harshal Kumar. (2025). NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 824-832.
Chicago Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. "NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 824-832.
Oxford Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. 'NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 824-832. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/NEUROIMAGE_BASED_STROKE_IDENTIFICATION__A_MACHINE_LEARNING_APPROACH_ijariie26029.pdf (Accessed : 04 April 2025).
Harvard Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. (2025) 'NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 824-832IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/NEUROIMAGE_BASED_STROKE_IDENTIFICATION__A_MACHINE_LEARNING_APPROACH_ijariie26029.pdf (Accessed : 04 April 2025)
IEEE Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar, "NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 824-832, Mar-App 2025. [Online]. Available: http://ijariie.com/AdminUploadPdf/NEUROIMAGE_BASED_STROKE_IDENTIFICATION__A_MACHINE_LEARNING_APPROACH_ijariie26029.pdf [Accessed : 04 April 2025].
Turabian Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. "NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 (04 April 2025).
Vancouver Omprasad Narkhede , Sujal Godse, Gavande Sudev Ankush , Akash Kakad, and Prof. Harshal Kumar. NEUROIMAGE-BASED STROKE IDENTIFICATION: A MACHINE LEARNING APPROACH. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : 04 April 2025]; 11(2) : 824-832. Available from: http://ijariie.com/AdminUploadPdf/NEUROIMAGE_BASED_STROKE_IDENTIFICATION__A_MACHINE_LEARNING_APPROACH_ijariie26029.pdf
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