Brain Stroke Prediction using Machine Learning Techniques. A Comparative Study

Scientific Study from the year 2023 in the subject Computer Science - Bioinformatics, grade: 10, VIT University (VIT), course: Computer Science, language: English, abstract: The use of machine learning for stroke prediction represents a powerful tool in enhancing patient care and reducing stroke-related mortality and disability. By focusing on key risk factors and leveraging extensive healthcare data, machine learning can substantially improve the accuracy and effectiveness of stroke prediction. This project aims to harness the potential of machine learning to better identify individuals at high risk of suffering a stroke and provide them with early, targeted interventions, ultimately saving lives and improving patient outcomes. The importance of predicting strokes cannot be overstated. Strokes are a leading cause of mortality and disability worldwide. Early detection and prevention can have a substantial impact on patient outcomes. Leveraging machine learning algorithms for stroke prediction can significantly improve the accuracy and efficacy of identifying high-risk patients. The primary objective of this project is to develop a precise stroke prediction system that can recognize high-risk patients based on a wide range of risk factors, including age, gender, medical history, lifestyle choices, and genetic factors. By creating a reliable model for stroke prediction, healthcare professionals can administer early interventions, potentially reducing stroke incidence and improving patient outcomes. The project's scope includes analyzing electronic health record (EHR) data to identify the key elements essential for stroke prediction. EHRs contain valuable information, including patient demographics, medical history, clinical findings, and other factors relevant to constructing a stroke prediction model. Machine learning for stroke prediction involves several stages. Initially, a dataset of relevant variables potentially influencing stroke occurrence is identified.