BRAIN STROKE PREDICTION SYSTEM

Abstract

ABSTRACT A brain stroke prediction system (100) is disclosed, comprising a set of sensors (102) configured to capture physiological metrics including blood pressure, heart rate, and oxygen saturation. A data processor (104) processes and transmits this data to a cloud-based machine learning module (106), which analyses patterns using algorithms such as logistic regression and neural networks to predict stroke risk. An alert module (108) generates notifications for timely medical intervention.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to predictive health monitoring systems and, more specifically, to a machine learning-based system for analysing physiological and behavioural metrics to predict the risk of brain strokes in individuals.
BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Brain stroke is a leading cause of death and disability globally, with significant long-term effects on patients and their families. Early prediction and intervention in individuals at high risk of s , Claims:1. A brain stroke prediction system (100), comprising:
a plurality of sensors (102) configured to monitor physiological metrics, including blood pressure, heart rate, oxygen saturation, and behavioral patterns;
a data processor (104) coupled to the sensors, wherein the data processor filters and processes the physiological data in real time and transmits the data wirelessly to a cloud-based platform;
a machine learning module (106) hosted on the cloud-based platform, programmed to analyse physiological data patterns in real-time and predict stroke risk based on pre-trained machine learning algorithms; and
an alert module (108) that generates notifications based on detected stroke risk and transmits the notifications to a healthcare provider or the patient's mobile device for timely intervention.

2. The stroke prediction system (100) as claimed in claim 1, wherein the machine learning module is configured to use a combination of logistic regression, support vector machines, and deep neural networks to enhanc

Documents