Heart Stroke Prediction
TL;DR
The proposed system reduces manual assessment time and provides quick decision support and offers an efficient, accurate, and cost-effective solution for heart stroke risk prediction.
Abstract
The Heart Stroke Prediction System is a machine learning-based application developed to predict the risk of heart stroke using patient health information. The system helps users identify potential stroke risks at an early stage through intelligent data analysis. It considers important health parameters such as age, gender, hypertension, heart disease, BMI, smoking status, glucose level, and work type. The collected data is preprocessed and analyzed to improve prediction accuracy. A Logistic Regression algorithm is used to classify whether a person is likely to experience a stroke or not. The model is trained using historical healthcare data and evaluated with testing datasets. The application is developed using Python and Flask to provide a simple and user-friendly web interface. Users can enter their health details and receive instant prediction results. The system also includes a BMI calculator and basic health guidance to improve awareness. It supports early diagnosis and encourages preventive healthcare measures. The proposed system reduces manual assessment time and provides quick decision support. It offers an efficient, accurate, and cost-effective solution for heart stroke risk prediction. Overall, this project demonstrates how machine learning can assist healthcare professionals and individuals in improving early stroke detection and promoting better health management.