A Novel Approach to Predict Brain Stroke using KNN in Machine Learning

Predicting stroke in humans using machine learning (ML) is an important healthcarepractice that has the potential to revolutionize prevention and bftyearly intervention strategies. Comprehensive thorough patient information, encompassing demographics, medical history, andlifestyle factors, and pertinentclinical tests, are being systematically collected and analyzed as partof this endeavor. ML algorithms, including logistic regression, decision trees, and neural networks, areleveraged to build predictive models, using key characteristics such as hypertension, age, and cholesterol levels. Through careful training, evaluation, and tuning of these models on large data sets, they become proficient at identifying people at risk of stroke. Deploying such models in clinical settings allows healthcare professionals to conduct real-time risk assessments, facilitating proactive interventions that cansignificantly reduce devastating effects of stroke. However, ensuring compliance with strict ethical and regulatory standards remains paramount to protecting patient privacy and maintaining the highest standards of healthcare practice