Stroke_prediction using Machine learning
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Modern technologies allow for noninvasive medical aid. Stroke is the most fatal of the four major cardiovascular conditions, yet early detection can save a patient's life. Using machine learning techniques, this project implements a clean method for the early identification of strokes with the help ...learn more
Project status: Published/In Market
Intel Technologies
oneAPI,
Intel CPU
Overview / Usage
A stroke, sometimes called a brain attack, occurs when something blocks blood supply to part of the brain or when a blood vessel in the brain bursts. In either case, parts of the brain become damaged or die. A stroke can cause lasting brain damage, long-term disability, or even death.Earlier treatment results in a greater chance of recovery, a reduced likelihood of permanent disability and lesser need for extensive rehabilitation.Disease Prediction using Machine Learning is the system that is used to predict the diseases from the symptoms which are given by the patients or any user. The system processes the symptoms provided by the user as input and gives the output as the probability of the disease.
Methodology / Approach
The dataset used is obtained from kaggle,it is imported to the notebook.There are some missing values found in the data and is replaced with mean value. One hot encoding is performed to convert the categorical values to a format suitable for the machine learning models.We have splitted the data into train and test sets and validated it with four different machine learning algorithms.
Technologies Used
The whole project is done using python language in jupyter notebook with oneAPI toolkit imported. The project is performed in Intel core i5 8th generation.
Repository
https://github.com/AkshayRamakrishnann/Stroke_prediction