Abstract:
Stroke is one of the leading causes of disability in many Asian countries, with low and
middle-income countries bearing a higher burden of mortality. Worldwide Cerebrovascular
accidents (stroke) are the second leading cause of death and the third leading cause of
disability, where in Bangladesh it is the third leading cause of death. Effective prevention
strategies include targeting the key modifiable factors like hypertension, diabetes,
smoking, and high cholesterol. Due to the high cost of our diagnosis system, the majority
of our people cannot go for checkups. Nowadays, IoT has unarguably transformed the
healthcare industry and is highly beneficial for doctors, and patients. This project proposes
a prototype IoT-based Brain Stroke Prediction System by analyzing the key risk factors of
stroke and predicting the associated risk status using machine learning technology. Blood
glucose level, hypertension status, heart disease status, smoking status, marital status, age,
gender, BMI, working type, and residence type are the risk factors employed for this
proposed system. The proposed prototype is able to detect blood glucose level which is one
of the key risk factors of stroke non-invasively using NIR spectrology. It then analyzes
other key risk factors of stroke along with blood glucose level using machine learning
technology. The final outcome of this proposed protype is the associative risk status of a
person in positive (high) or negative (low) format.
