Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare

Title

Enhancing Stroke Prediction: Leveraging Ensemble Learning for Improved Healthcare

Creator

James E.; Jacob L.; Reddy K.B.

Description

Stroke, a potentially deadly medical disorder, requires excellent prediction and prevention measures to minimize its impact on individuals and healthcare systems. In this study, ensemble learning techniques are employed to enhance the accuracy of stroke prediction. The method combines four different machine learning algorithms, Adaboost, CatBoost, XGBoost, and LightGBM, to produce a strong predictive model. The data was composed of a rich set of demographic, medical, and lifestyle information. The data was preprocessed and features were engineered to maximize predictive performance. Results showed that the stacked ensemble model, which is composed of Adaboost, CatBoost, XGB, LightGBM, and Logistic Regression, meta-model, outperformed other models. The model has the potential to be used as a decision support tool in an early stroke risk assessment system, enhancing clinician decision-making and improving healthcare outcomes. 2024 IEEE.

Source

2024 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Bagging; Ensemble Learning; Machine Learning; Stacking; Stroke Prediction

Coverage

James E., CHRIST(Deemed to be University), Data Science, Lavasa, India; Jacob L., CHRIST(Deemed to be University), Data Science, Lavasa, India; Reddy K.B., CHRIST(Deemed to be University), Data Science, Lavasa, India

Rights

Restricted Access

Relation

ISBN: 979-835037024-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCCNT61001.2024.10725209

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211187039&doi=10.1109%2fICCCNT61001.2024.10725209&partnerID=40&md5=9ad2b2a9cbfcab860755f54f62522c14