The project aims at predicting whether a patient is likely to get a stroke based on the input parameters like gender, age, BMI, average glucose level, various diseases like hypertension, heart disease, and smoking status. (Course: CSL2050 Pattern Recognition and Machine Learning)

🩺 Machine Learning applied to stroke prediction for unbalanced data

Data Mining Techniques for Stroke and Email Spam Prediction

An end-to-end machine learning project for stroke prediction

Some machine learning algorithms to predict whether a person would have a stroke or not.

Stroke is a condition that happens when the blood flow to the brain is impaired or diminished. It is also referred to as Brain Circulatory Disorder. It is now possible to predict when a stroke will start by using ML approaches thanks to advancements in medical technology.

This project aims to make predictions of stroke cases based on simple health data. Supervised machine learning algorithm was used after processing and analyzing the data. The model has predicted Stroke cases with 92.00% of sensitivity.

Data analysis on Dataset of patients who had a stroke (Sklearn, pandas, seaborn)

A Machine Learning Analysis of Stroke Data and Accessible Website

This project hence helps to predict the stroke risk using prediction model and provide personalized warning and the lifestyle correction message. By doing so, it also urges medical users to strengthen the motivation of health management and induce changes in their health behaviors.

The project aims at displaying the charts/plots of the number of people affected by stroke based on the input parameters like smoking status, high blood pressure level, Cholesterol level, obesity level in some of the countries.

A patient's chance of stroke was predicted using analysis of a medical dataset. Project conducted in collaboration with a partner, Phillis Duong.

Project work on #Healthcare_stroke_prediction using #MySQL

Utilising a publicly-available and small dataset of ~5K patients from Kaggle, to practice health data analysis.

Regression Analysis using dataset from different Industries

94% accuracy, can predict the liklihood of a stroke in a patient.

Stroke Prediction comparative analysis on various machine learning models

A web application developed with Django for real-time stroke prediction using logistic regression.