Research Article Early Stroke Prediction Methods for Prevention of Strokes Mandeep Kaur , 1 Sachin R. Sakhare , 2 Kirti Wanjale , 2 and Farzana Akter 3 1 Department of Computer Science, Savitribai Phule Pune University, Pune, India 2 Computer Engineering Department, Vishwakarma Institute of Information Technology, Kondhwa (Bk), Pune, India 3 Department of ICT, Bangabandhu Sheikh Mujibur Rahman Digital University, Bangladesh Correspondence should be addressed to Farzana Akter; farzana@ict.bdu.ac.bd Received 26 November 2021; Revised 2 March 2022; Accepted 21 March 2022; Published 11 April 2022 Academic Editor: Suresh Satapathy Copyright © 2022 Mandeep Kaur et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients. 1. Introduction Nowadays, due to technological advancements, life expec- tancy of human being is rising day by day. The lifestyle has been changed from active lifestyle to sedentary lifestyle due to the advent of technical gadgets such as laptops, smart phones, and portable devices. Not only the aging society but the young generation is also facing many health prob- lems such as cardiovascular diseases, diabetes, hypertension, and strokes due to inactive lifestyle. There is a need of smart health-care devices to monitor the health of individuals by using some biomarkers and noninvasive smart techniques. The studies in the exiting literature produce evidences of bad impact of sedentary lifestyle on human health [1–3]. In this paper, we are focusing on the problem of strokes, and an attempt is made to devise a system which uses bio- electrical images to predict the strokes. The major cause behind stroke is disruption of blood supply due to clotting in the blood to the nerves in the brain. The stroke can be major or minor. In minor stroke, the blood supply to some parts of the brain is hampered, and in major stroke, the per- son can lose life. Stroke is an emergency health condition which has to be dealt with carefully. The common symptoms include trouble in movement, confusions, improper verbal communication, and difficulty in understanding. Stroke causes long-term neurological damage and death. There are two categories of stroke, ischemic embolic and hemorrhagic. Ischemic embolic stroke occurs when there is a blood lump at heart and not in the brain, and it narrows the brain arteries. In hemorrhagic stroke, there is blood leakage Hindawi Behavioural Neurology Volume 2022, Article ID 7725597, 9 pages https://doi.org/10.1155/2022/7725597