Predicting Brain Stroke Risk Using Machine Learning and Deep Learning Models.

This project aims to develop a comprehensive system for the classification and prediction of brain strokes using advanced machine learning and deep learning techniques The project encompasses data preprocessing, data visualization, and the implementation of various algorithms to achieve high accuracy in stroke prediction. Data preprocessing involves cleaning and preparing datasets, addressing missing values, and performing feature engineering to enhance model performance. Data visualization techniques are employed to uncover insightful patterns and trends related to stroke, aiding in the interpretation of complex medical data. In addition to traditional machine learning algorithms, this system incorporates deep learning techniques using Convolutional Neural Networks (CNNs) for effective stroke detection from imaging data. CNNs are utilized to extract spatial features and detect stroke-related anomalies in medical images, significantly improving the system’s diagnostic accuracy. The integration of CNN-based deep learning ensures robust analysis, especially when dealing with high-dimensional data such as brain scans. The complete solution is deployed within the Django web framework, providing a user-friendly interface for medical professionals. This web application enables healthcare providers to make informed, real-time decisions based on predictive analytics and model outputs, thus contributing to timely stroke diagnosis and intervention.