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@article{158219,
        author = {Abhishek Khot and Megha Patil},
        title = {Review on Stroke Prediction Mobile App using Machine Learning Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {9},
        pages = {386-389},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158219},
        abstract = {stroke is the third largest leading cause of death and the principal cause of serious long-term disability in the United States.  According to the world health organization (WHO) stroke is the second largest cause of death globally responsible for approximately 11% of the total death. Accurate prediction of stroke is highly valuable for early intervention and treatment.
A stroke is caused when blood flow to part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the aera of the brain affected. Early recognition of the symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. Stroke is third largest leading cause of death and the principal cause of serious long-term disability in the United States. According to the world health organization (WHO) stroke is the second largest cause of death globally responsible for approximately 11% of the total deaths. Accurate prediction of the stroke is highly valuable for early intervention and treatment.  In research work with help of the machine learning, several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence further this machine learning models can be used in backend for developing mobile applications so that real time monitoring of the health could be done using some sensors and input data from user.
There are numbers of mobile application exits which detect chances of any person getting stroke using some health-related information. Most of the mobile application uses machine learning algorithm in backend to predict the chances of getting stroke by proving the health-related data as input to the algorithms.
In this paper we are going to look at some of the previous mobile application and machine learning algorithm which are used for predicting chances of getting stroke. We will look at some important factors that matters the most for accurate prediction of stroke and how can we improve machine learning algorithm to make accurate prediction of stroke also some further work related to using some external monitoring devices to get real-time input data such as wearable devic},
        keywords = {},
        month = {},
        }