Promoting Brain Health Through Machine Learning to Predict Stroke Risk

Abstract

Stroke is a serious disease that can lead to serious consequences if not diagnosed and treated in time. The project focuses on using machine learning algorithms to analyze different types of data, including medical history, lifestyle and physical characteristics, to create powerful predictive models. Methods include preprocessing and data cleaning, product removal, and options to improve model accuracy. Various machine learning algorithms such as decision trees, support vector machines, and neural networks will be applied and compared to determine which is best for batting prediction [1]. The model will be trained with historical data and its performance will be evaluated with metrics such as demand, specificity and accuracy. The potential impact of this study includes early identification of individuals at high risk of stroke and provision of timely intervention and prevention. The development of effective predictive models can help reduce the burden of stroke-related morbidity and mortality. By combining architectural models with health applications, the program demonstrates the collaboration of today's technology in solving important health problems. Key Words: Brain Strokes, Early Detection, Predictive Model, Machine Learning Techniques, Medical Histories, Lifestyle Factors, Physiological Parameters, Preprocessing, Feature Extraction, Feature Selection, ML Algorithms, Healthcare Applications, Engineering Principles, Interdisciplinary Approach.