A Novel Machine Learning based Stroke Prediction System using Magnetic Resonance Imaging and Adaptive New Fuzzy Inference System

Authors

  • Ajanthaa Lakkshmanan Assistant Professor, Department of Computing Technologies, SRM Institute of Science and Technology, Chennai-603 202.
  • Adaline Suji R. Associate Professor grade-1, Scope, Vellore Institute of Technology, Vellore campus 632 014.
  • Priyanka N. Assistant Professor senior grade-1, Scope, Vellore Institute of Technology, Vellore campus – 632
  • D. Bright Anand Professor, Department of Computer Science and Engineering, Narayana Engineering College, Gudur, Tirupati District, 524101.Andhra Pradesh.
  • Komatigunta Nagaraju Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Dist, A. P, India.
  • U. Ganesh Naidu Assistant professor,CSBS, B V Raju Institute Of Technology, Narsapur, Medak, Telangana, India-502313

Keywords:

ANFIS, Brain, CapsuleNet, Prediction, SegNet, Stroke

Abstract

Smart health analytics is a highly researched field that employs the power and intelligence of technology for efficient treatment and prevention of several diseases.  We are currently living in the post COVID phase, which has seen a tremendous rise in sudden deaths caused by many neurological diseases, among which stroke is the major one.  It is considered to be the second largest causative disease of death amongst human population according to the World Health Organization.  Hence this paper proposes a new method for predicting the onset of stroke using the machine learning approach of Adaptive Neuro Fuzzy Inference System (ANFIS).  The input data set for stroke prediction is obtained from Kaggle data repository called as the Brain Stroke prediction dataset which contains 5111 electronic health records of patients with 11 different parameters related to the stroke disease along with brain MRI images.  The data obtained is preprocessed using data cleaning methods, segmented using SegNet and features relevant are extracted using CapsuleNet. Predictive analytics is done using ANFIS model and is compared with existing classifiers like Logistic Regression, Random Forest, XG Boost algorithm, Adaboost algorithm and Gated Recurrent Unit.  The predictive performance of the proposed model is tested using metrics like accuracy, precision, sensitivity, specificity, F1 measure and ROC curve analysis.

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References

G, T.R., Bhattacharya, S., Maddikunta, P.K.R. et al, “Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset,” Multimedia Tools and Applications, vol.81, pp. 41429–41453, 2022. 10.1007/s11042-020-09988-y.

Pedersen A, Stanne TM, Nilsson S, Klasson S, Rosengren L, Holmegaard L, Jood K, Blennow K, Zetterberg H, Jern C, “Circulating neurofilament light in ischemic stroke: temporal profile and outcome prediction,” Journal of neurology, vol.266, no.11, pp.2796-2806, 2019. 10.1007/s00415-019-09477-9

Kwon HS, Lee D, Lee MH, Yu S, Lim JS, Yu KH, Oh MS, Lee JS, Hong KS, Lee EJ, Kang DW, Kwon SU; PICASSO investigators, “post-stroke cognitive impairment as an independent predictor of ischemic stroke recurrence: PICASSO sub-study,” Journal of neurology, vol.267, pp.688-693, 2020. 10.1007/s00415-019-09630-4

Wang H, Sun Y, Ge Y, Wu PY, Lin J, Zhao J, Song B (2021), “A clinical-radiomics nomogram for functional outcome predictions in ischemic stroke,” Neurology and Therapy, vol.10, no.2, pp.819-832. 10.1007/s40120-021-00263-2.

Yu, Y., Parsi, B., Speier, W., Arnold, C., Lou, M., Scalzo, F. (2019). LSTM Network for Prediction of Hemorrhagic Transformation in Acute Stroke. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11767. Springer, Cham. 10.1007/978-3-030-32251-9_20

Liu, L., Chen, S., Zhang, F., Wu, F. X., Pan, Y., & Wang, J., “Deep convolutional neural network for automatically segmenting acute ischemic stroke lesion in multi-modality MRI,” Neural Computing and Applications, vol.32, pp.6545-6558, 2020. 10.1007/s00521-019-04096-x

Ananya, P.R., Pachisia, V., Ushasukhanya, S. (2022). Optimization of CNN in Capsule Networks for Alzheimer’s Disease Prediction Using CT Images. In: Manogaran, G., Shanthini, A., Vadivu, G. (eds) Proceedings of International Conference on Deep Learning, Computing and Intelligence. Advances in Intelligent Systems and Computing, vol 1396. Springer, Singapore. 10.1007/978-981-16-5652-1_49

Anusha Bai, R., Sangeetha, V. (2023). A Comparative Study on Brain Intracerebral Hemorrhage Classification Using Head CT Scan for Stroke Analysis. In: Ranganathan, G., EL Allioui, Y., Piramuthu, S. (eds) Soft Computing for Security Applications. ICSCS 2023. Advances in Intelligent Systems and Computing, vol 1449. Springer, Singapore. 10.1007/978-981-99-3608-3_44

Zhang, Y., Yu, M., Tong, C., Zhao, Y., & Han, J., “CA-UNet Segmentation Makes a Good Ischemic Stroke Risk Prediction,” Interdisciplinary Sciences: Computational Life Sciences, pp.1-15. 2023. 10.1007/s12539-023-00583-x.

Weng, YT., Chan, HW., Huang, TY. (2020). Automatic Segmentation of Brain Tumor from 3D MR Images Using SegNet, U-Net, and PSP-Net. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science, vol 11993. Springer, Cham. 10.1007/978-3-030-46643-5_22

Odeh, S., Kyriacou, E., & Pattichis, C. S. (2023). Using ANFIS to Predict the Long- and Short-Term Stroke Risk Based on Ultrasound Carotid Imaging and Clinical data of Initially Asymptomatic Patients. Journal of Millimeterwave Communication, Optimization and Modelling, vol.3, no.1, pp.27-31, 2023.

Pathanjali, C., Monisha, G., Priya, T., Ruchita, S. K., & Bhaskar, S, “Machine learning for predicting ischemic stroke,” International Journal of Engineering Research & Technology, vol.9, no.5, pp.1-4, 2020. 10.17577/IJERTV9IS050836

Bali, B., & Garba, E. J, “Neuro-fuzzy approach for prediction of neurological disorders: a systematic review,” SN Computer Science, vol.2, no.4, pp.307, 2021. 10.1007/s42979-021-00710-9

Dev, S., Wang, H., Nwosu, C. S., Jain, N., Veeravalli, B., & John, D, “A predictive analytics approach for stroke prediction using machine learning and neural networks,” Healthcare Analytics, vol.2, pp.100032, 2022. 10.1016/j.health.2022.100032

Wu, Y., & Fang, Y., “Stroke prediction with machine learning methods among older Chinese,” International journal of environmental research and public health, vol.17, no.6, pp.1828, 2020. 10.3390/ijerph17061828

Sailasya, G., & Kumari, G. L. A., “Analyzing the performance of stroke prediction using ML classification algorithms,” International Journal of Advanced Computer Science and Applications, vol.12 no.6, pp. 539-545, 2021. 10.14569/IJACSA.2021.0120662

Dritsas, E., & Trigka, M., “Stroke risk prediction with machine learning techniques,” Sensors, vol.22, no.13, pp.1-13, 2022. 10.3390/s22134670

Vasukidevi, G., Ushasukhanya, S., & Mahalakshmi, P, “Efficient image classification for alzheimer’s disease prediction using capsule network,” Annals of the Romanian Society for Cell Biology, vol.25, no.5, pp.806-815, 2021.

Manoharan, J. S., Braveen M. & Ganesan Subramanian, S, “A hybrid approach to accelerate the classification accuracy of cervical cancer data with class imbalance problems,” International Journal of data mining, vol.25, no.3-4, pp. 234 – 259, 2021. 10.1504/IJDMB.2021.122865

Yu, Y., Xie, Y., Thamm, T., Gong, E., Ouyang, J., Huang, C., ... & Zaharchuk, G, “Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging,” JAMA network open, vol.3, no.3, pp. 1-13, 2020.10.1001/jamanetworkopen.2020.0772

Lakkshmanan, Ajanthaa, Anbu Ananth, C., and Tiroumalmouroughane, S, “Multi-objective Metaheuristics with Intelligent Deep Learning Model for Pancreatic Tumor Diagnosis,” Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 6793-6804, 2022. 10.3233/JIFS-221171

Chauhan, S., Vig, L., De Filippo De Grazia, M., Corbetta, M., Ahmad, S., & Zorzi, M, “A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images,” Frontiers in Neuro informatics, vol.13, no.53, pp. 1-12, 2019. 10.3389/fninf.2019.00053

Hu, X., Luo, W., Hu, J., Guo, S., Huang, W., Scott, M. R., ... & Reyes, M, “Brain SegNet: 3D local refinement network for brain lesion segmentation,” BMC medical imaging, vol.20, pp.1-10, 2020. 10.1186/s12880-020-0409-2

Bacchi, S., Zerner, T., Oakden-Rayner, L., Kleinig, T., Patel, S., & Jannes, J, “Deep learning in the prediction of ischaemic stroke thrombolysis functional outcomes: a pilot study,” Academic radiology, vol.27, no.2, pp. e19-e23, 2020. 10.1016/j.acra.2019.03.015

Liu, T., Fan, W., & Wu, C, “A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset,” Artificial intelligence in medicine, vol.101, pp.1-30, 2019. 10.1016/j.artmed.2019.101723

Sathya, V, Mahendra Babu, G.R.; Ashok, J.; Lakkshmanan, Ajanthaa. “A Novel Predicting Students Performance Approach to Competency & Hidden Risk Factor Identifier Using a Various Machine Learning Classifiers,” Journal of Intelligent & Fuzzy Systems, vol. 44, no.6, pp. 9565–9579, 2023. 10.3233/JIFS-224586

Heo, J., Yoon, J. G., Park, H., Kim, Y. D., Nam, H. S., & Heo, J. H, “Machine learning–based model for prediction of outcomes in acute stroke,” Stroke, vol.50, no.5, pp.1263-1265, 2019. 10.1161/STROKEAHA.118.024293

Dehdar Karsidani, S., Farhadian, M., Mahjub, H., & Mozayanimonfared, A, “Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model,” BMC Cardiovascular Disorders, vol.22, no.1, pp.1-8, 2022. 10.1186/s12872-022-02825-0

Mohanty, R., Solanki, S.S., Mallick, P.K., Pani, S.K. (2021). A Classification Model Based on an Adaptive Neuro-fuzzy Inference System for Disease Prediction. In: Bhoi, A., Mallick, P., Liu, CM., Balas, V. (eds) Bio-inspired Neurocomputing. Studies in Computational Intelligence, vol 903. Springer, Singapore. 10.1007/978-981-15-5495-7_7

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Published

07.01.2024

How to Cite

Lakkshmanan, A. ., Suji R., A. ., N., P. ., Anand, D. B. ., Nagaraju, K. ., & Naidu, U. G. . (2024). A Novel Machine Learning based Stroke Prediction System using Magnetic Resonance Imaging and Adaptive New Fuzzy Inference System. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 576–585. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4410

Issue

Vol. 12 No. 10s (2024)

Section

Research Article

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