Early Detection of Stroke for Ensuring Health and Well-Being Based on Categorical Gradient Boosting Machine
DOI:
https://doi.org/10.5614/itbj.ict.res.appl.2022.16.3.8Keywords:
CatBoost, gradient boosting machine, health, stroke, stroke prediction, well-beingAbstract
Stroke is believed to be among the leading causes of adult disability worldwide. It is wreaking havoc on African people, families, and governments, with ramifications for the continent?s socio-economic development. On the other hand, stroke research output is insufficient, resulting in a dearth of evidence-based and context-driven guidelines and strategies to combat the region?s expanding stroke burden. Indeed, for African and other developing economies to meet the UN Sustainable Development Goals (SDGs), particularly SDG 3, which aims to guarantee healthy lifestyles and promote well-being for people of all ages, the issue of stroke must be addressed to reduce early death from non-communicable illnesses. This study sought to create a robust predictive model for early stroke diagnosis using an understandable machine learning (ML) technique. We implemented a categorical gradient boosting machine model for early stroke prediction to protect patients? health and well-being. We compared the effectiveness of our proposed model to existing state-of-the-art machine learning models and previous studies by empirically testing it on a real-world public stroke dataset. The proposed model outperformed the others when compared to the other methods using the research data, achieving the maximum accuracy (96.56%), the area under the curve (AUC) (99.73%), F1-measure (96.68%), recall (99.24%), and precision (93.57%). Functional outcome prediction models based on machine learning for stroke were verified and shown to be adaptable and helpful.
Downloads
References
Hilbert, A., Ramos, L.A., van Os, H.J.A., Olabarriaga, S.D., Tolhuisen, M.L., Wermer, M.J.H., Barros, R.S., van der Schaaf, I., Dippel, D., Roos, Y.B.W.E.M., van Zwam, W.H., Yoo, A.J., Emmer, B.J., Lycklama Nijeholt, G.J., Zwinderman, A.H., Strijkers, G.J., Majoie, C.B.L.M. & Marquering, H.A., Data-Efficient Deep Learning of Radiological Image Data for Outcome Prediction after Endovascular Treatment of Patients with Acute Ischemic Stroke, Comput. Biol. Med., 115, 103516, 2019. DOI: 10.1016/j.compbiomed.2019.103516.
Sirsat, M.S., Ferm E. & Cara, J., Machine Learning for Brain Stroke: A Review, J. Stroke Cerebrovasc. Dis., 29(10), 105162, 2020. DOI: 10.1016/j.jstrokecerebrovasdis.2020.105162.
Qiu, W., Kuang, H., Teleg, E., Ospel, J.M., Il Sohn, S., Almekhlafi, M., Goyal, M., Hill, M.D., Demchuk, A.M. & Menon, B.K., Machine Learning for Detecting Early Infarction in Acute Stroke with Non?Contrast-enhanced CT, Radiology., 294, pp. 638-644, 2020. DOI: 10.1148/radiol.2020191193.
Donkor, E.S., Owolabi, M.O., Bampoh, P., Aspelund, T. & Gudnason, V., Community Awareness of Stroke in Accra, Ghana, BMC Public Health, 14, 196, 2014.
Kashi, S., Polak, R.F., Lerner, B., Rokach, L. & Levy-Tzedek, S., A Machine-Learning Model for Automatic Detection of Movement Compensations in Stroke Patients, IEEE Trans. Emerg. Top. Comput., 9(3), pp. 1234-1247, 2021. DOI: 10.1109/TETC.2020.2988945.
Chang, C.Y., Cheng, M.J. & Ma, M.H.M., Application of Machine Learning for Facial Stroke Detection, in: Int. Conf. Digit. Signal Process. DSP, IEEE, pp. 1-5 , 2019. DOI: 10.1109/ICDSP.2018.8631568.
Guberina, N., Dietrich, U., Radbruch, A., Goebel, J., Deuschl, C., Ringelstein, A., Krmann, M., Kleinschnitz, C., Forsting, M. & Mninghoff, C., r fvc of Early Infarction Signs with Machine Learning-Based Diagnosis by Means of the Alberta Stroke Program Early CT Score (ASPECTS) in the Clinical Routine, Neuroradiology. 60, pp. 889?901, 2018. DOI: 10.1007/s00234-018-2066-5.
Feigin, V.L., Forouzanfar, M.H., Krishnamurthi, R., Mensah, G.A., Connor, M., Bennett, D.A., Moran, A.E., Sacco, R.L., Anderson, L., Truelsen, T., O?Donnell, M., Venketasubramanian, N., Barker-Collo, S., Lawes, C.M.M., Wang, W., Shinohara, Y., Witt, E., Ezzati, M., Naghavi, M. & Murray, C., Global and Regional Burden of Stroke during 1990-2010: Findings from the Global Burden of Disease Study 2010, Lancet, 383, pp. 245?255, 2014. DOI: 10.1016/S0140-6736(13)61953-4.
Akinyemi, R.O. & Brainin, M., The African Stroke Organization - A New Dawn for Stroke in Africa, Nat. Rev. Neurol., 17, pp. 127-128, 2021. DOI: 10.1038/s41582-021-00456-1.
Ezejimofor, M.C., Uthman, O.A., Maduka, O., Ezeabasili, A.C., Onwuchekwa, A.C., Ezejimofor, B.C., Asuquo, E., Chen, Y.F., Stranges, S. & Kandala, N.-B., Stroke Survivors in Nigeria: A Door-To-Door Prevalence Survey from the Niger Delta Region, J. Neurol. Sci., 372, pp. 262-269, 2017. DOI: 10.1016/j.jns.2016.11.059.
Owolabi, M., Akarolo-Anthony, S., Akinyemi, R., Arnett, D., Gebregziabher, M., Jenkins, C., Tiwari, H., Arulogun, O., Akpalu, A., Sarfo, F., Obiako, R., Owolabi, L., Sagoe, K., Melikam, S., Adeoye, A., Lackland, D. & Ovbiagele, B., The Burden of Stroke in Africa: A Glance at the Present and a Glimpse Into The Future: Review Article, Cardiovasc. J. Afr., 26, pp. S27?S38, 2015. DOI: 10.5830/CVJA-2015-038.
Sampane-Donkor, E., A Study of Stroke in Southern Ghana : Epidemiology, Quality of Life and Community Perceptions, Doctoral Dissertation, Reykjav: University of Iceland, School of Health Sciences, 2014.
Akinyemi, R.O., Owolabi, M.O., Ihara, M., Damasceno, A., Ogunniyi, A., Dotchin, C., Paddick, S.-M., Ogeng?o, J., Walker, R. & Kalaria, R.N., Stroke, Cerebrovascular Diseases and Vascular Cognitive Impairment in Africa, Brain Res. Bull. 145, pp. 97-108, 2019. DOI: 10.1016/j.brainresbull.2018.05.018.
Sarfo, F.S., Akpa, O., Ovbiagele, B., Akpalu, A., Wahab, K., Komolafe, M., Obiako, R., Owolabi, L., Osaigbovo, G.O., Jenkins, C., Ogbole, G., Fakunle, A., Tiwari, H.K., Arulogun, O., Arnett, D.K., Asowata, O., Ogah, O., Akinyemi, R.O. & Owolabi, M.O., Influence of Age on Links Between Major Modifiable Risk Factors and Stroke Occurrence in West Africa, J. Neurol. Sci., 428, 117573, 2021. DOI: 10.1016/j.jns.2021.117573.
Sarfo, F.S. & Ovbiagele, B., Prevalence and Predictors of Statin Utilization among Patient Populations at High Vascular Risk in Ghana, J. Neurol. Sci., 414, 116838, 2020. DOI: 10.1016/j.jns.2020.116838.
Sailasya, G. & Kumari, G.L.A., Analyzing the Performance of Stroke Prediction using ML Classification Algorithms, Int. J. Adv. Comput. Sci. Appl., 12, pp. 539-545, 2021. DOI: 10.14569/IJACSA.2021.0120662.
Emon, M.U., Keya, M.S., Meghla, T.I., Rahman, M.M., Al Mamun, M.S. & Kaiser, M.S., Performance Analysis of Machine Learning Approaches in Stroke Prediction, in: 2020 4th Int. Conf. Electron. Commun. Aerosp. Technol., IEEE, pp. 1464-1469, 2020. DOI: 10.1109/ICECA49313.2020.9297525.
Akyeramfo-Sam, S., Addo Philip, A., Yeboah, D., Nartey, N.C. & Kofi Nti, I., A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks, Int. J. Inf. Technol. Comput. Sci., 11, pp. 54-60, 2019. DOI: 10.5815/ijitcs.2019.11.06.
Nti, I.K., Nyarko-Boateng, O. & Aning, J., Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation, Int. J. Inf. Technol. Comput. Sci., 13, pp. 61-71, 2021. DOI: 10.5815/ijitcs.2021.06.05.
Mench-Lara, R.M. & Sancho-Gez, J.L., Fully Automatic Segmentation of Ultrasound Common Carotid Artery Images Based on Machine Learning, Neurocomputing, 151, pp. 161-167, 2015. DOI: 10.1016/j.neucom.2014.09.066.
Lau, H., Tong, K. & Zhu, H., Support Vector Machine for Classification of Walking Conditions of Persons after Stroke with Dropped Foot, Hum. Mov. Sci., 28, pp. 504-514. 2009. DOI: 10.1016/j.humov.2008.12.003.
Khosla, A., Cao, Y., Lin, C.C.Y., Chiu, H.K., Hu, J. & Lee, H., An Integrated Machine Learning Approach to Stroke Prediction, Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 183-191, 2010. DOI: 10.1145/1835804.1835830.
Singh, M.S., Choudhary, P. & Thongam, K., A Comparative Analysis for Various Stroke Prediction Techniques, in Int. Conf. Comput. Vis. Image Process., Springer, Singapore, pp.98-106, 2019.
Bandi, V., Bhattacharyya, D. & Midhunchakkravarthy, D., Prediction of Brain Stroke Severity Using Machine Learning, Rev. d?Intelligence Artif. 34, pp. 753-761, 2020. DOI: 10.18280/ria.340609.
Nwosu, C.S., Dev, S., Bhardwaj, P., Veeravalli, B. & John, D., Predicting Stroke from Electronic Health Records, Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS., pp. 5704-5707, 2019. DOI: 10.1109/EMBC.2019.8857234.
Nti, I.K., Adekoya, A.F. & Weyori, B.A., A Novel Multi-Source Information-Fusion Predictive Framework Based on Deep Neural Networks for Accuracy Enhancement in Stock Market Prediction, J. Big Data, 8, 17, 2021. DOI: 10.1186/s40537-020-00400-y.
Nti, I.K., Adekoya, A.F. & Weyori, B.A., A Systematic Review of Fundamental and Technical Analysis of Stock Market Predictions, Artif. Intell. Rev., 53, pp. 3007-3057, 2019. DOI: 10.1007/s10462-019-09754-z.
Almadani, O. & Alshammari, R., Prediction of Stroke using Data Mining Classification Techniques, Int. J. Adv. Comput. Sci. Appl. 9(1), pp. 453-460, 2018. DOI: 10.14569/IJACSA.2018.090163.
Kansadub, T., Thammaboosadee, S., Kiattisin, S. & Jalayondeja, C., Stroke Risk Prediction Model Based on Demographic Data, in 8th Biomedical Engineering International Conference (BMEiCON), 2015.
Jamthikar, A., Gupta, D., Saba, L. Khanna, N.N., Araki, T., Viskovic, K., Mavrogeni, S. Laird, J.R., Pareek, G., Miner, M., Sfikakis, P.P., Protogerou, A., Viswanathan, V., Sharma, A., Nicolaides, A., Kitas, G.D. & Suri, J.S., Cardiovascular/Stroke Risk Predictive Calculators: A Comparison between Statistical and Machine Learning Models, Cardiovasc. Diagn. Ther., 10(4), pp. 919-938, 2020. DOI: 10.21037/cdt.2020.01.07.
Shanthi, D., Sahoo, G. & Saravanan, N., Designing an Artificial Neural Network Model for the Prediction of Thromboembolic Stroke, Int. J. Biometrics Bioinforma, 3(1), pp. 10-18, 2009.
Kim, S.H., Jeon, E.T., Yu, S., Oh, K., Kim, C.K., Song, T.J., Kim, Y.J., Heo, S.H., Park, K.Y., Kim, J.M., Park, J.H., Choi, J.C., Park, M.S., Kim, J.T., Choi, K.H., Hwang, Y.H., Kim, B.J., Chung, J.W., Bang, O.Y., Kim, G., Seo, W.K. & Jung, J.M., Interpretable Machine Learning for Early Neurological Deterioration Prediction in Atrial Fibrillation-Related Stroke, Sci. Rep., 11, pp. 1-9, 2021. DOI: 10.1038/s41598-021-99920-7.
Park, D., Jeong, E., Kim, H., Pyun, H.W., Kim, H., Choi, Y.-J., Kim, Y., Jin, S., Hong, D., Lee, D.W., Lee, S.Y. & Kim, M.C. Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea, Diagnostics, 11(10), 1909, 2021. DOI: 10.3390/diagnostics11101909.
Dorogush, A.V., Ershov, V. & Gulin, A., Catboost: Gradient Boosting with Categorical Features Support, ArXiv, abs/1810.11363, pp. 1-7, 2018.
Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W. & Zhou, H., Evaluation of Catboost Method for Prediction of Reference Evapotranspiration in Humid Regions, J. Hydrol, 574, pp. 1029-1041, 2019. DOI: 10.1016/j.jhydrol.2019.04.085.
Hancock, J. & Khoshgoftaar, T.M., Medicare Fraud Detection using CatBoost, Proc. - 2020 IEEE 21st Int. Conf. Inf. Reuse Integr. Data Sci. IRI 2020, pp. 97-103, 2020. DOI: 10.1109/IRI49571.2020.00022.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A., Catboost: Unbiased Boosting with Categorical Features, Adv. Neural Inf. Process. Syst. 2018-Decem, pp. 6638?6648, 2018.
Kang, P., Lin, Z., Teng, S., Zhang, G., Guo, L. & Zhang, W., Catboost-Based Framework with Additional User Information for Social Media Popularity Prediction, MM 2019 - Proc. 27th ACM Int. Conf. Multimed., pp. 2677-2681, 2019. DOI: 10.1145/3343031.3356060.
Breiman, L., Random Forest, Mach. Learn, 45, pp. 5-32, 2001. DOI: 10.1023/A:1010933404324.
Vapnik, V.N., An Overview of Statistical Learning Theory, IEEE Trans. Neural Networks, 10, pp. 988-999, 1999. 10.1109/72.788640.
Horak, J., Vrbka, J. & Suler, P., Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison, J. Risk Financ. Manag., 13, 60, 2020. DOI: 10.3390/jrfm13030060.
Nti, I.K., Nyarko-Boateng, O., Adekoya, F.A. & Weyori, B.A., An Empirical Assessment of Different Kernel Functions on the Performance of Support Vector Machines, Bull. Electr. Eng. Informatics., 10(6), pp.3403-3411, 2021. DOI: 10.11591/eei.v10i6.3046.
Sarfo, F.S., Agyei, M., Ogyefo, I., Opare-Addo, P.A. & Ovbiagele, B., Factors Linked to Chronic Kidney Disease Among Stroke Survivors in Ghana, J. Stroke Cerebrovasc. Dis., 30, pp. 1-7, 2021. DOI: 10.1016/j.jstrokecerebrovasdis.2021.105720.
