PREDICTING STROKES USING A HYBRID STROKE PREDICTION TECHNIQUE BASED ON ENSEMBLE LEARNING (HSPEL)

Registration ID: IJNRD_205522

Published ID: IJNRD2309251

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Deepan L

Strokes, Prediction, Hybrid,Predicting

The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. With an increased synergy between technology and medical diagnosis, caregivers create opportunities for better patient management by systematically mining and archiving the patients’ medical records. Therefore, it is vital to study the interdependency of these risk factors in patients’ health records and understand their relative contribution to stroke prediction. This paper systematically analyzes the various factors in electronic health records for effective stroke prediction. Using various statistical techniques and ensemble learning, the most important factors for stroke prediction are assessed and the patients’ dataset samples classified. The proposed scheme called HSPELs (Heart Stroke Prediction with Ensemble Learning) predicts the risks of patients being affected with heart strokes. The schema achieved an accuracy of approximately 93 percent in classifying samples that are like to be affected by stroke..

Published Paper   E-Certificate

Deepan L (September-2023). PREDICTING STROKES USING A HYBRID STROKE PREDICTION TECHNIQUE BASED ON ENSEMBLE LEARNING (HSPEL). INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(9), c453-c459. https://ijnrd.org/papers/IJNRD2309251.pdf

Volume 8 Issue 9, September-2023

Pages : c453-c459

Paper Reg. ID: IJNRD_205522

Published Paper Id: IJNRD2309251

Downloads: 000121998

Research Area: Electrical Engineering 

Author Type: Indian Author

Country: Thiruvallur District, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2309251.pdf

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