TY  - JOUR
AU  - T Sai Lalith Prasad
AU  - K Saiprakash
AU  - M Ashok Reddy
AU  - M Varshitha
PY  - 2025
DA  - 2025/06/27
TI  - Predictive Analysis to Find Chances of Causing Stroke (Machine Learning)
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 4
AB  - Stroke is recognised as one of the most dangerous it can cause for both life time disability and cause
immediate death,across the world, there is a immediate necessary of accurate predictive models.
These machine learning models are used to more accurate identifying the person who are more likely to
experiencing this life altering medical event. So, now there is need for prediction of stroke for present
generation intervention and based on the result taking before medication. Stroke can be predicted
by analysing different signs in your body like hypertension, severe headache, trouble speaking and
numbness on the face arm or on legs. By giving some health parameters like age, hypertension(0,1),
any heart disease, married, residence type, average glucose level are considered as the input feature
which is used to training the model and testing the model this helps to predict the given inputs and
predict accurate solution. In this model we used algorithms like Linear Support Vector Machine (SVM)
for classification, SMOTEENN (Synthetic Minority Over-sampling Technique with Edited Nearest
Neighbors) used to balance the dataset, One-Hot Encoding is used to preprocess categorical variables
by converting them into a numerical format, Pipeline combines preprocessing and the classification
algorithm into a single workflow. This model can give approximately 85-90% accuracy
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1140
DO  - 10.33425/3066-1226.1140