Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

Mudita Uppal; Deepali Gupta; Sapna Juneja; Thippa Reddy Gadekallu; Ibrahim El Bayoumy; Jamil Hussain; Seung Won Lee

doi:10.3389/fbioe.2023.1257591

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

Front Bioeng Biotechnol. 2023 Sep 25:11:1257591. doi: 10.3389/fbioe.2023.1257591. eCollection 2023.

Authors

Mudita Uppal¹, Deepali Gupta¹, Sapna Juneja², Thippa Reddy Gadekallu^{3

4

5

6

7}, Ibrahim El Bayoumy⁸, Jamil Hussain⁹, Seung Won Lee¹⁰

Affiliations

¹ Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
² KIET Group of Institutions, Ghaziabad, India.
³ Zhongda Group, Jiaxing, Zhejiang, China.
⁴ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
⁵ School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India.
⁶ College of Information Science and Engineering, Jiaxing University, Jiaxing, China.
⁷ Division of Research and Development, Lovely Professional University, Phagwara, India.
⁸ Public Health and Community Medicine, Tanta Faculty of Medicine, Tanta, Egypt.
⁹ Department of Data Science, Sejong University, Seoul, Republic of Korea.
¹⁰ Sungkyunkwan University School of Medicine, Suwon, Republic of Korea.

Abstract

The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health. This paper proposed a technique to predict brain strokes with high accuracy. The model was constructed using data related to brain strokes. The aim of this work is to use Multi Layer Perceptron (MLP) as a classification technique for stroke data and used multi-optimizers that include Adaptive moment estimation with Maximum (AdaMax), Root Mean Squared Propagation (RMSProp) and Adaptive learning rate method (Adadelta). The experiment shows RMSProp optimizer is best with a data training accuracy of 95.8% and a value for data testing accuracy of 94.9%. The novelty of work is to incorporate multiple optimizers alongside the MLP classifier which offers a comprehensive approach to stroke prediction, providing a more robust and accurate solution. The obtained results underscore the effectiveness of the proposed methodology in enhancing the accuracy of brain stroke detection, thereby paving the way for potential advancements in medical diagnosis and treatment.

Keywords: AdaMax; Adadelta; RMSProp; deep learning; heart stroke; multi layer perceptron.

Grants and funding

The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Research Foundation of Korea (grant number NRF 2021R1I1A2059735).