Optimizing Stroke Risk Prediction Using Symptom-Based  Feature Selection

Bhuiyan, Refat Ahmed; Sarkar, Ahnaf Abid; Bisma, Tahya Ahammed

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dc.contributor.author	Bhuiyan, Refat Ahmed
dc.contributor.author	Sarkar, Ahnaf Abid
dc.contributor.author	Bisma, Tahya Ahammed
dc.date.accessioned	2025-02-28T05:41:28Z
dc.date.available	2025-02-28T05:41:28Z
dc.date.issued	2024-06-25
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dc.identifier.uri	http://hdl.handle.net/123456789/2327
dc.description	Supervised by Mr. Ahmad Shafiullah, Department of Electrical and Electronic Engineering (EEE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Electrical and Electronic Engineering, 2024	en_US
dc.description.abstract	Stroke is a significant health concern, with early detection being challenging. Our research employs symptom-based feature selection using chi-square analysis and RFECV. By applying a logistic regression algorithm, we achieved 93% accuracy with just 9 features.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Electrical and Elecrtonics Engineering(EEE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.subject	Stroke, Machine Learning,Symptoms,Chi-Square,RFECV,	en_US
dc.title	Optimizing Stroke Risk Prediction Using Symptom-Based Feature Selection	en_US
dc.type	Thesis	en_US