Hybrid Machine Learning Approach for Early Stroke Prediction in Elderly People

Stroke represents a significant global health challenge, often leading to severe disability and mortality. The timely prediction and intervention of stroke are paramount in enhancing patient outcomes and reducing healthcare burdens. This project proposes a machine learning-based approach to predict stroke risk using multi-modal biosignals, specifically electrocardiogram (ECG) data. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system aims to classify patient health data into critical risk categories such as Normal, Abnormal, Ischemic, and Hemorrhagic. The study utilizes a comprehensive dataset consisting of ECG signals and incorporates techniques for data preprocessing, class balancing, and feature extraction. The predictive model is trained and validated using robust evaluation metrics, including accuracy, precision, recall, and F1-score. The findings underscore the efficacy of the proposed system in providing real-time stroke risk assessments, offering a cost-effective alternative to traditional diagnostic methods. Furthermore, this research explores the integration of wearable technology with machine learning, highlighting its potential for continuous patient monitoring and early detection of stroke symptoms. By creating a user-friendly interface for healthcare professionals and patients, the system aims to facilitate prompt decision-making and intervention, ultimately improving the overall quality of care for individuals at risk of stroke