Stroke is a disease that affects the arteries leading to and within the brain. A stroke occurs when a blood vessel that carries oxygen and nutrients to the brain is either blocked by a clot or ruptures. According to the WHO, stroke is the 2nd leading cause of death worldwide. Globally, 3% of the population are affected by subarachnoid hemorrhage, 10% with intracerebral hemorrhage, and the majority of 87% with ischemic stroke. 80% of the time these strokes can be prevented, so putting in place proper education on the signs of stroke is very important. The existing research is limited in predicting risk factors pertained to various types of strokes. Early detection of stroke is a crucial step for efficient treatment and ML can be of great value in this process. To be able to do that, Machine Learning (ML) is an ultimate technology which can help health professionals make clinical decisions and predictions. During the past few decades, several studies were conducted on the improvement of stroke diagnosis using ML in terms of accuracy and speed. The existing research is limited in predicting whether a stroke will occur or not. Machine Learning techniques including Random Forest, KNN , XGBoost , Catboost and Naive Bayes have been used for prediction.Our work also determines the importance of the characteristics available and determined by the dataset.Our contribution can help predict early signs and prevention of this deadly disease

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Introduction In my case studies I keep writing in English because it is used in Kaggle and I also keep them in Portuguese because my native language is Brazilian Portuguese, so we can share more knowledge and experiences in Kaggle with our Brazilian colleagues. We will develop and analyze the algorithms with the best capacity and identify the problems in the heart and at the end we will make a comparison between them. Description Context Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Four out of 5CVD deaths are due to heart attacks and strokes, and one-third of these deaths occur prematurely in people under 70 years of age. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict a possible heart disease. People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. Attribute Information Age: age of the patient [years] Sex: sex of the patient [M: Male, F: Female] ChestPainType: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic] RestingBP: resting blood pressure [mm Hg] Cholesterol: serum cholesterol [mm/dl] FastingBS: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise] RestingECG: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria] MaxHR: maximum heart rate achieved [Numeric value between 60 and 202] ExerciseAngina: exercise-induced angina [Y: Yes, N: No] Oldpeak: oldpeak = ST [Numeric value measured in depression] ST_Slope: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping] HeartDisease: output class [1: heart disease, 0: Normal] Source This dataset was created by combining different datasets already available independently but not combined before. In this dataset, 5 heart datasets are combined over 11 common features which makes it the largest heart disease dataset available so far for research purposes. The five datasets used for its curation are: Cleveland: 303 observations Hungarian: 294 observations Switzerland: 123 observations Long Beach VA: 200 observations Stalog (Heart) Data Set: 270 observations Total: 1190 observations Duplicated: 272 observations Final dataset: 918 observations Every dataset used can be found under the Index of heart disease datasets from UCI Machine Learning Repository on the following link: https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/ Citation fedesoriano. (September 2021). Heart Failure Prediction Dataset. Retrieved [Date Retrieved] from https://www.kaggle.com/fedesoriano/heart-failure-prediction. Acknowledgements Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Machine Learning is the fastest-growing technique in many fields and the healthcare industry is no exception to this. Machine Learning algorithms plays an essential role in predicting the presence/absence of Heart diseases, tumors, and more. Such required information, if predicted well in advance, can provide important insights to doctors who can then adapt their diagnosis and treat the patient accordingly. World Health Organization has estimated 12 million deaths occur worldwide, every year due to heart diseases. Half the deaths in the United States and other developed countries are due to cardiovascular diseases. The early prognosis of stroke diseases can aid in making decisions on lifestyle changes in high-risk patients and in turn reduce the complications. If it is about to identify the relationship and factors affecting it can cured n advance time. This research intends to pinpoint the most relevant/risk factors of heart disease as well as predict the overall risk using logistic regression. In this report, I'll discuss the prediction of stroke using Machine Learning algorithms. The algorithm I have implemented is logistic regression on the Health

Stroke Risk Prediction Using Machine Learning Algorithms

A real-time stroke risk prediction system integrating machine learning, IoT sensors (MAX30102 + NodeMCU), and a web-based UI. Live vitals (heart rate, SpO₂) are transmitted via MQTT to a Flask backend, combined with user health inputs, and analyzed using a Random Forest model for instant risk assessment.

Brain strokes are a leading cause of disability and death worldwide. Early prediction of stroke risk can help in taking preventive measures. This project focuses on building a Brain Stroke Prediction System using Machine Learning algorithms, Flask for backend API development, and React.js for the frontend.

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MATLAB-based stroke risk prediction using EEG signals and machine learning

Developed a Heart Stroke Prediction model using Machine Learning to analyze health data and predict stroke risk with high accuracy.

AI-powered Stroke Risk Prediction System using Machine Learning and Deep Learning for early detection and preventive care.

This is a stroke risk prediction wep app build by using Machine Learning , HTMl , CSS , and Flask

A web-based platform developed to provide services like patient reported stroke risk prediction using machine learning

Comparison of SVM, KNN, DT, and LightGBM Algorithms for Accurate Stroke Risk Prediction Using Machine Learning Classification Techniques

🧠 Machine Learning model for stroke prediction. Uses patient data to predict stroke risk based on medical parameters. Built with Python & ML libraries.

Machine learning-based diabetes detection and stroke risk prediction using EDA, SMOTE, cross-validation, and model evaluation, deployed via Streamlit.

Stroke Risk Prediction using Big Data Analytics & Machine Learning. Built an end-to-end pipeline with data preprocessing, feature engineering, and hyperparameter-tuned models. Deployed interactive dashboards (Gradio) for real-time stroke risk prediction and insights.

StrokeHero is a comprehensive system for stroke risk prediction, combining data engineering, machine learning, and web application components. The system processes medical data, particularly ECG signals, to predict stroke risk using advanced deep learning models.

This study evaluates stroke risk prediction using XGBoost optimized by the Zebra Optimization Algorithm. By enhancing hyperparameter tuning, the proposed model improves accuracy and efficiency, offering a promising machine learning approach for early stroke risk assessment.

This project focuses on stroke prediction and analysis using machine learning techniques for early risk assessment. It utilizes medical and lifestyle data, applying preprocessing and feature selection to build accurate models. A user-friendly interface presents stroke risk predictions to support timely intervention.

This is an end-to-end prediction project built using Machine Learning + Flask + Tailwind CSS. The goal is to predict stroke risk based on patient health data.

This repository publishes the models for risk predictions of 30-day mortality after stroke using machine learning and statistical methods. The models were developed and validated with SSNAP (Stroke registry in UK) and externally validated with RiksStroke (Stroke registry in Sweden).

Early heart stroke prediction project using machine learning and healthcare analytics. Features EDA, Logistic Regression, Random Forest, recall optimization, risk factor analysis, and an interactive Streamlit dashboard

Early prediction of stroke risk can save lives by enabling timely medical intervention. This project uses machine learning to analyze health indicators and identify individuals at high risk. Accurate predictions help doctors prioritize patients and plan preventive care effectively.

“This dissertation uses machine learning to predict brain stroke risk using clinical and lifestyle data from Mendeley. After preprocessing and handling class imbalance, multiple models were tested, with Random Forest performing best. The study highlights both the potential and limitations of ML in stroke prediction.

The Health Prediction System is a web application that helps users assess their risk for various health conditions, including heart disease, diabetes, and stroke (brain disease). Using Machine Learning models, the system takes user input, processes it, and provides a health risk assessment along with personalized recommendations.

This Brain Stroke Prediction application is designed to help identify individuals who may be at risk of experiencing a stroke. Built using machine learning and a user-friendly front-end interface, the application takes into account various health and demographic factors such as age, hypertension, heart disease, glucose level, BMI, smoking status, a

A machine learning–based web application that predicts the risk of heart disease/stroke using clinical patient data. The project combines data analysis, feature engineering, model training, and a Streamlit-based user interface to deliver real-time predictions in a user-friendly format.

This project is a Flask-based web application that predicts the risk of brain stroke using machine learning and provides AI-powered preventive advice. It combines traditional data-driven prediction with modern generative AI, offering users both analytical insights and friendly, evidence-based lifestyle suggestions.

We are a team of data scientists passionate about using machine learning to solve real-world healthcare problems. This repository contains our capstone project on stroke risk prediction — 15 models trained, evaluated, and deployed on patient clinical data. Built with Python, scikit-learn, LightGBM, and imbalanced-learn. | Team Nexus | Cohort 5