Advanced Stroke Detection and Alert System using Machine Learning

Stroke has a significant socioeconomic impact on the world. Ischemic strokes arise when a blood clot (also known as "thrombi") or a fatty plaque (made up of fat residue, cholesterol, and waste particles) blocks the blood supply to a part of the brain, killing the neurons in that area (brain cells). The majority of stroke patients will survive the first sickness, the long-term implications for individuals generally have the largest impact on their health and may also lead to larger numbers of death. So, the prior detection of stroke is necessary to prevent the condition. A cloud-based Stroke prediction system has been presented to identify Stroke using a Machine learning approach in order to diagnose it at an early stage. The intention of the design is to use the convolution neural network (CNN) algorithm to design an automated early ischemic stroke disclosure system. The major goal of using CNN is to accurately detect a stroke and alert the doctor or career as soon as possible.

Autorentext

Malini Thangavelu, Professora Assistente na Faculdade de Engenharia e Tecnologia Sri Krishna. Concluiu a sua investigação no domínio do sistema de energia e publicou artigos em revistas na área da engenharia eléctrica.