Machine Learning Based Stroke Predictor Application
DOI:
https://doi.org/10.4108/eetiot.5384Keywords:
Stroke, SVM, Healthcare, Machine Learning, MLAbstract
When blood flow to the brain stops or slows down, brain cells die because they don't get enough oxygen and nutrients. This condition is known as an ischemic stroke. It is now the biggest cause of death in the whole planet. Examining the afflicted people has shown a number of risk variables that are thought to be connected to the stroke's origin. Numerous studies have been conducted to predict the illnesses associated with stroke using these risk variables. The prompt identification of various warning symptoms associated with stroke has the potential to mitigate the severity of the stroke. The utilization of machine learning techniques yields prompt and precise predictive outcomes. Although its uses in healthcare are expanding, certain research domains have a stronger need for more study. We think that machine learning algorithms may aid in a deeper comprehension of illnesses and make an excellent healthcare partner. The textual dataset of numerous patients, which includes many medical variables, is gathered for this study. The missing values in the dataset are located and dealt with during processing. The dataset is used to train machine learning algorithms including Random Forest, Decision Tree classifier, and SVM. The method that delivers the greatest accuracy for our dataset is then selected once the accuracy of the algorithms has been determined. This aids patients in determining the likelihood of a brain stroke and ensuring they get the right medical attention.
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Kanna, R. K., Devi, K. Y., Dhivy, A. J. A., Amutha, P. M. D., Gomalavalli, R., & Ambikapathy, A. (2022, December). Software Development Framework for Cardiac Disease Prediction Using Machine Learning Applications. In 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/CCET56606.2022.10080016
Li, C., Liu, X., Shen, P., Sun, Y., Zhou, T., Chen, W., ... & Gao, P. (2023). Improving Cardiovascular Risk Prediction through Machine Learning Modelling of Irregular Repeated Electronic Health Records. European Heart Journal-Digital Health, ztad058. DOI: https://doi.org/10.1093/ehjdh/ztad058
Amagada, P. U. (2023, October). An Inferable Machine Learning Approach for Reservoir Lithology Characterization Using Drilling Data. In SPE Annual Technical Conference and Exhibition? (p. D011S999R007). SPE. DOI: https://doi.org/10.2118/217485-STU
Sun, W., Fang, Y., Wang, J., Yin, D., Chao, X., Zhang, F., ... & Liu, X. (2023). Post-stroke apathy biotypes and their relation to the effort-based reward decision network: a resting-state fMRI study. DOI: https://doi.org/10.21203/rs.3.rs-3248784/v1
Gutiérrez-Serafín, B., Andreu-Perez, J., Pérez-Espinosa, H., Paulmann, S., & Ding, W. (2024). Toward assessment of human voice biomarkers of brain lesions through explainable deep learning. Biomedical Signal Processing and Control, 87, 105457. DOI: https://doi.org/10.1016/j.bspc.2023.105457
Raj, R., Kannath, S. K., Mathew, J., & Sylaja, P. N. (2023). AutoML accurately predicts endovascular mechanical thrombectomy in acute large vessel ischemic stroke. Frontiers in Neurology, 14. DOI: https://doi.org/10.3389/fneur.2023.1259958
Lee, J., Kwon, K., Soltis, I., Matthews, J., Lee, Y., Kim, H., ... & Yeo, W. H. (2023). Intelligent upper-limb exoskeleton using deep learning to predict human intention for sensory-feedback augmentation. arXiv preprint arXiv:2309.04655.
Kanna, R. K., Kripa, N., & Vasuki, R. Systematic Design Of Lie Detector System Utilizing EEG Signals Acquisition. International Journal of Scientific & Technology Research, 9, 610-2.
Sharma, N., Mangla, M., Iqbal, M. M., & Mohanty, S. N. (2023). Deep Learning Framework for Identification of Skin Lesions. EAI Endorsed Transactions on Pervasive Health and Technology, 9(1). DOI: https://doi.org/10.4108/eetpht.9.3900
Mondal, S., Ghosh, S., & Nag, A. (2023). Brain stroke prediction model based on boosting and stacking ensemble approach. International Journal of Information Technology, 1-10. DOI: https://doi.org/10.1007/s41870-023-01418-0
Mohanty, S. N., Diaz, V. G., & Kumar, G. S. (Eds.). (2023). Intelligent Systems and Machine Learning: First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I (Vol. 470). Springer Nature.
Kanna, R. K., Mutheeswaran, U., Ramya, V. S., Gomalavalli, R., Hema, L. K., & Ambikapathy, A. (2022, December). Computing Model for Alzheimer Prediction Using Support Vector Machine Classifier. In 2022 IEEE International Conference on Current Development in Engineering and Technology (CCET) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/CCET56606.2022.10080346
Kanna, R.K., Ishaque, M., Panigrahi, B.S., Pattnaik, C.R. (2024). Prediction of Covid-19 Using Artificial Intelligence [AI] Applications. In: Roy, B.K., Chaturvedi, A., Tsaban, B., Hasan, S.U. (eds) Cryptology and Network Security with Machine Learning. ICCNSML 2022. Algorithms for Intelligent Systems. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-99-2229-1_30
Singh, R., Sharma, H. K., Choudhury, T., Mor, A., Mohanty, S., & Mohanty, S. N. (2023). Blockchain for IoT-enabled Healthcare. EAI Endorsed Transactions on Pervasive Health and Technology, 9. DOI: https://doi.org/10.4108/eetpht.9.3348
Rahman, A., Rahman, F. B. A., Islam, A., Jahan, I., & Salam, K. M. A. (2023, July). Cerebral Stroke Prediction Using Machine Learning Algorithms. In 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/ICECCME57830.2023.10252689
Kanna, R. K., Gomalavalli, R., Devi, Y., & Ambikapathy, A. (2023). Computational Cognitive Analysis for Intelligent Engineering Using EEG Applications. In Intelligent Engineering Applications and Applied Sciences for Sustainability (pp. 309-350). IGI Global. DOI: https://doi.org/10.4018/979-8-3693-0044-2.ch017
Kanna, R. K., Surendhar, S. P. A., AL-Hameed, M. R., Lafta, A. M., Khalid, R., & Hussain, A. (2023, May). Smart Prosthetic Arm Using Cognitive Application. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1330-1334). IEEE. DOI: https://doi.org/10.1109/ICACITE57410.2023.10182794
Bajaj, K., & Jain, R. (2023, July). Teeth Staining from Tobacco Consumption as an Indicator to Chronic Illness: A Data Analytics and Machine Learning Application. In 2023 World Conference on Communication & Computing (WCONF) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/WCONF58270.2023.10234993
R. K. Kanna, V. Subha Ramya, A. A. Khafel, K. A. Jabbar, M. Al-Tahee and R. Khalid, "Cognitive Disability Prediction & Analysis using Machine Learning Application," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 1359-1364.
Naveen, S., Upamanyu, M. S., Chakki, K., Chandan, M., & Hariprasad, P. (2023, July). Air Quality Prediction Based on Decision Tree Using Machine Learning. In 2023 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ICSSES58299.2023.10200535
Ravikumar, K. K., Ishaque, M., Panigrahi, B. S., & Pattnaik, C. R. (2023). Detection of Covid-19 Using AI Application. EAI Endorsed Transactions on Pervasive Health and Technology, 9. DOI: https://doi.org/10.4108/eetpht.9.3349
Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6
Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9.https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016
Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937
Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023.https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052
Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603
Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579
Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484
Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69.https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069
Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470
Rahman, A., Hoque, S., Khan, M. T., Rahman, F. B. A., Islam, A., Shumona, S. I., & Salam, K. M. A. (2023, June). Early Prediction of Ischemic Stroke Using Machine Learning Boosting Algorithms. In 2023 3rd International Conference on Intelligent Technologies (CONIT) (pp. 1-7). IEEE. DOI: https://doi.org/10.1109/CONIT59222.2023.10205861
Kanna, R. K., & Vasuki, R. (2019). Advanced Study of ICA in EEG and Signal Acquisition using Mydaq and Lab view Application. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN, 2278-3075.
Kaur, R., & Dhillon, S. (2023). Brain Stroke Prediction System–A Review. International Journal of Modern Developments in Engineering and Science, 2(6), 21-24.
Kanna, R. K., Ramya, V. S., Khafel, A. A., Jabbar, K. A., Al-Tahee, M., & Khalid, R. (2023, May). Cognitive Disability Prediction & Analysis using Machine Learning Application. In 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 1359-1364). IEEE. DOI: https://doi.org/10.1109/ICACITE57410.2023.10182534
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