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A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction

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Healthcare Analytics 2024年 6卷 100362-100362页

作者： Ahmed, Rasel Fahad, Nafiz Miah, Md Saef Ullah Hossen, Md. Jakir Morol, Md. Kishor Mahmud, Mufti Mostafizur Rahman, M. Faculty of Science and Technology American International University-Bangladesh Kuratoli Khilkhet Dhaka 1229 Bangladesh Faculty of Information Science and Technology (FIST) Multimedia University Melaka Malaysia Faculty of Engineering and Technology (FET) Multimedia University Jalan Ayer Keroh Lama 75450 Bukit Beruang Melaka Malaysia Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Nottingham NG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Nottingham NG11 8NS United Kingdom Department of Mathematics American International University-Bangladesh Dhaka 1229 Bangladesh Department of Computing and Information Science Cornell University NY United States EliteLab.Ai NY United States DataTech Research and Innovation Learning Lab Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model's effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model's predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation. © 2024 The Authors

关键词： Dementia prediction Early detection Logistic regression Recursive feature elimination Shapley Additive exPlanations (SHAP)

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