An Improved Machine Learning Approach for Predicting Ischemic Stroke
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : N. Suresh Kumar, M. Thangamani, V. Sasikumar, S. Nallusamy
|DOI : 10.14445/22315381/IJETT-V69I1P217|
MLA Style: N. Suresh Kumar, M. Thangamani, V. Sasikumar, S. Nallusamy. "An Improved Machine Learning Approach for Predicting Ischemic Stroke" International Journal of Engineering Trends and Technology 69.1(2021):111-115.
APA Style:N. Suresh Kumar, M. Thangamani, V. Sasikumar, S. Nallusamy. An Improved Machine Learning Approach for Predicting Ischemic Stroke International Journal of Engineering Trends and Technology, 69(1), 111-115.
Stroke occurs when appearing the blockage of tissues of the brain of blood coagulation. This obstruction can show up at the collar or in the brain. Clot, as a rule, begins in the heart and moves by the circulatory context. Clotting can be distinct all unaided or get stopped in a strip. When it obstructs a mind corridor, the cerebrum doesn`t get enough blood or oxygen, and cells begin to die. Stroke is the major cause of blockage of blood in tissues where the brain`s oxygen and blood supply. Enters of disease control and prevention decide stroke is one of the main reasons for death. In the US, 7 95,000 peoples have a stroke in the year 2020. The major symptoms are trouble walking, trouble speaking, loss of balance, blurred vision, etc. This research mainly focuses on finding the stroke and the major types of strokes. Stroke is a major role in the peoples who have suffered more. This research uses Recursive Feature Elimination using the Cross-Validation (RFECV) algorithm to eliminate similar strokes.
 V. Prasanna and M. Thangamani, Cancer subtype discovery using prognosis-enhanced neural network classifier in metagenomic data, Technology in Cancer Research & Treatment, 17(2018) 1-15.
 M. Thangamani, R. Vijayalakshmi, M. Ganthimathi, M. Ranjita, P. Malarkodi, and S. Nallusamy, Efficient classification of heart disease using a k-means clustering algorithm, International Journal of Engineering Trends and Technology, 68(12)(2020) 48-53.
 S.K. Muruganandham et al., Study on leaf segmentation using K-means and K-medoid clustering algorithm to identify disease, Indian Journal of Public Health Research and Development, 9(5)(2018) 289-293.
 Senthilkumar Mohan et al., Can machine learning improve cardiovascular risk prediction using routine clinical data? PLoS One, 12(4)(2017) 1-9.
 Joon Nyung Heo et al., Machine learning-based model for predicting outcomes in acute stroke, Stroke,50 (2019) 6-12.
 Ravi Garg et al., Automating ischemic stroke subtype classification using machine learning and natural language processing, Journal of Stroke and Cerebrovascular Diseases, 28(7)(2019) 2045-2051.
 W. Liu, D. Li and H. Han, Anifold learning analysis for allele-skewed DNA modification SNPs for psychiatric disorders, IEEE Access, 8(2020) 75-83.
 X. Qiang et al., Using the spike protein feature to predict Infection risk and monitor coronavirus`s evolutionary dynamic, Infect Dis Poverty, 33(9)(2020) 156-162.
 F. Akhtar et al., Optimal features subset selection for large for gestational age classification using grid search based recursive feature elimination with cross-validation scheme, Frontier Computing, 551(2020) 63-71.
 D. Sobya, S. Manoj, Prediction and identification of cancer and normal genes through wavelet transform technique, Indian Journal of Public Health Research and Development, 10(8)(2019) 631-637.
 Y. Ge, Q. Wang, and L. Wang Predicting post-strike pneumonia using deep neural network approaches, Int. Journal of Medical Informatics, 132(2019) 98-105.
 Hilbert et al., Data efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke, Computers in Biology and Medicine, 115(2019) 1-7.
 H. Kuang et al., Automated ASPECTS on non-contrast ct scans in patients with acute ischemic stroke using machine learning, American Journal of Neuroradiology, 40(1)(2019) 33-38.
 A.N. Beecy et al., A novel deep learning approach for automated diagnosis of acute ischemic infarction on computed tomography, J Am Coll Cardiol Cardiovascular Imaging, 11(11)(2018) 1723-1725.
 Rajat Dhar et al., Application of machine learning to automated analysis of cerebral edema in large cohorts of ischemic stroke patients, Frontiers in Neurology, 9 (2018) 687-695.
 Asit Subudhi et al., Automated approach for detecting ischemic stroke using delaunay triangulation in brain MRI images, Medicine, 1 (2018) 116-129.
 Sunil A. Sheth et al., Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography, Stroke,50(11) (2019) 3093-3100.
 Ona Wu et al., Big Data approaches to phenotyping acute ischemic stroke using automated lesion segmentation of multi-center magnetic resonance imaging data, Stroke, 50 (2019) 1734-1741.
 S.F. Sung, C.Y. Lin and Hu, EMR-based phenotyping of ischemic stroke using supervised machine learning and text mining techniques, IEEE Journal of Biomedical and Health Informatics, 24(10) (2020) 2922-2931.
 Acharya et al., Automatic detection of ischemic stroke using higher-order spectra features in brain MRI images, Cognitive Systems Research, 58 (2019) 134-142.
 Subudhi, Dash and Sabut, Automated segmentation and classification of brain stroke using expectation maximization and random forest classifier, Bio Cybernetics & Biomedical Engg., 40(1) (2020) 277-289.
 Kellner et al., Comparison of automated and visual DWI ASPECTS in acute ischemic stroke, Journal of Neuroradiology, 46(5) (2019) 288-293.
 H. Kuang et al., Validation of an automated ASPECTS method on non-contrast computed tomography scans of acute ischemic stroke patients, International Journal of Stroke, 15(5)(2020) 528-534.
 W. Qiu et al., Machine learning for detecting early infarction in acute stroke with non-contrast-enhanced CT, Radiology, 294(3)(2020) 55-65.
 R. Zhang, et al., Automatic segmentation of acute ischemic stroke from DWI using 3-D fully convolutional Dense Nets, IEEE Transactions on Medical Imaging, 37(9) (2018) 2149-2160.
 R. Dhar, Automated quantitative assessment of cerebral edema after ischemic stroke using CSF volumetric, Neuroscience Letter, 724(2020) 1-9.
 H. Lee et al., Machine learning approach to identify stroke within 4.5 hours, Stroke, 51 (2020) 860-866.
 R. Feng et al., Deep learning guided stroke management: a review of clinical applications, Journal of Neurointerventional Surgery, 10(4)(2017) 358-362.
 N.M. Murray et al., Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review, Journal of Neurointerventional Surgery, 12(2)(2020) 156-164.
 O. Maier et al., ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI, Medical Image Analysis, 35(2017) 250-269.
 P. Hoelter et al., Automated ASPECT scoring in acute ischemic stroke: Comparing three software tools, Neuroradiology, 62(2020) 1231-1238.
 H. Kuang, B.K. Menon and W. Qiu, Semi-automated infarct segmentation from follow?up non-contrast CT scan in patients with acute ischemic stroke, Medical Physics Research, 46(9)(2019) 4037-4045.
 I.Q. Grunwald, J. Kulikovski, W. Reith and S. Gerry, Collateral automation for triage in stroke: aluating automated scoring of collaterals in acute stroke on computed tomography scans, Cerebrovascular Diseases, 47(2019) 217-222.
 ayakumar Sadhasivam, Senthil Jayavel, Arpit Rathore, Survey Of Genetic Algorithm Approach In Machine Learning, International Journal of Engineering Trends and Technology 68(2) (2020)115-133.
Ischemic Stroke, Machine Learning, IST Dataset, Elimination, Classification Modules, Prediction