Volume 3 • Issue 1 • PP: 51-59 • 2023
From Data to Diagnosis: Applied Machine Learning for Stroke Prediction in Computational Healthcare
Open Access & Copyright
© 2023 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
View PDF Abstract
Stroke is a leading cause of disability and mortality worldwide, emphasizing the need for accurate and timely prediction methods. In recent years, advancements in machine learning and computational healthcare have shown promising results in various medical domains. This paper presents a comprehensive study on the application of machine learning techniques for stroke prediction in computational healthcare. The objective of this research is to develop a robust and accurate stroke prediction model that can assist healthcare professionals in identifying individuals at high risk of stroke. Leveraging a diverse dataset consisting of demographic information, medical history, and clinical measurements, a range of machine learning algorithms is employed to extract meaningful patterns and relationships. Feature selection techniques are utilized to identify the most relevant predictors, ensuring optimal model performance. Through rigorous experimentation and evaluation, the proposed machine learning model demonstrates superior performance in stroke prediction compared to traditional risk assessment methods. The implications of this research extend beyond stroke prediction, with the proposed methods serving as a foundation for the development of similar predictive models in other healthcare domains.
Keywords
References
[1]. Ali, F., El-Sappagh, S., Islam, S. R., Kwak, D., Ali, A., Imran, M., & Kwak, K. S. (2020). A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, 208-222.
[2]. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
[3]. Chantamit-O-Pas, P., & Goyal, M. (2018). Long short-term memory recurrent neural network for stroke prediction. In Machine Learning and Data Mining in Pattern Recognition: 14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part I 14 (pp. 312-323). Springer International Publishing.
[4]. Nithya, B., & Ilango, V. (2017, June). Predictive analytics in health care using machine learning tools and techniques. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 492-499). IEEE.
[5]. Gupta, Deepa, Sangita Khare, and Ashish Aggarwal. "A method to predict diagnostic codes for chronic diseases using machine learning techniques." In 2016 International Conference on Computing, Communication and Automation (ICCCA), pp. 281-287. IEEE, 2016.
[6]. Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019). Artificial intelligence transforms the future of health care. The American journal of medicine, 132(7), 795-801.
[7]. Cheon, S., Kim, J., & Lim, J. (2019). The use of deep learning to predict stroke patient mortality. International journal of environmental research and public health, 16(11), 1876.
[8]. Mujumdar, A., & Vaidehi, V. (2019). Diabetes prediction using machine learning algorithms. Procedia Computer Science, 165, 292-299.
[9]. Induja, S. N., & Raji, C. G. (2019, March). Computational methods for predicting chronic disease in healthcare communities. In 2019 International Conference on Data Science and Communication (IconDSC) (pp. 1-6). IEEE.
[10]. Saber, H., Somai, M., Rajah, G. B., Scalzo, F., & Liebeskind, D. S. (2019). Predictive analytics and machine learning in stroke and neurovascular medicine. Neurological research, 41(8), 681-690.
[11]. Maini, E., Venkateswarlu, B., & Gupta, A. (2019). Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 (pp. 627-632). Springer International Publishing.
[12]. Sirsat, M. S., Fermé, E., & Câmara, J. (2020). Machine learning for brain stroke: a review. Journal of Stroke and Cerebrovascular Diseases, 29(10), 105162.
[13]. Dourado Jr, C. M., da Silva, S. P. P., da Nobrega, R. V. M., Barros, A. C. D. S., Reboucas Filho, P. P., & de Albuquerque, V. H. C. (2019). Deep learning IoT system for online stroke detection in skull computed tomography images. Computer Networks, 152, 25-39.
[14]. Muniasamy, A., Tabassam, S., Hussain, M. A., Sultana, H., Muniasamy, V., & Bhatnagar, R. (2020). Deep learning for predictive analytics in healthcare. In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) 4 (pp. 32-42). Springer International Publishing.
[15]. Gupta, S., & Sedamkar, R. R. (2019, March). Apply Machine Learning for Healthcare to enhance performance and identify informative features. In 2019 6th Internation
Cite This Article
Choose your preferred format
Publisher's Note
The statements, opinions, and data presented in this article are solely those of the author(s) and do not necessarily represent those of ASPG, the journal, or its editors. ASPG and the editors disclaim responsibility for any harm arising from the use of any ideas, methods, instructions, or products described in this article, to the fullest extent permitted by applicable law.