Work

Prediction of Stroke by using Machine learning techniques

Year: 2026

Type: article

Abstract: Stroke is a blood clot or bleeds in the brain, which can make permanent damage that has an effect on mobility, cognition, sight or communication. Stroke is considered as medical urgent situation and can cause long-term neurological damage, complications and often death. In this study, we propose early prediction of stroke diseases using different machine learning approaches with the occurrence of hypertension, body mass index level, heart disease, average glucose level, smoking status, previous stroke and age. Using these high features attributes, different classifiers have been trained, namely: Logistics Regression, Random Forest Classifier, K-Nearest Neighbors Classifier, and Support Vector Machine for predicting the stroke. And we observe that Random forest classifier has highest accuracy among them

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18639528

Authors: Ragini Patil, Dr. R. R. Kumbhar, Dr. S.V. Kakade

Institution: Krishna Institute of Medical Sciences Deemed University

Language: English

Topic: Artificial Intelligence in Healthcare

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7135415055

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