Citation

Ahmed, Rasel and Fahad, Nafiz and Miah, Md Saef Ullah and Hossen, Md. Jakir and Morol, Md. Kishor and Mahmud, Mufti and Mostafizur Rahman, M. (2024) A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction. Healthcare Analytics, 6. p. 100362. ISSN 2772-4425

Text A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Dementia
Subjects:	B Philosophy. Psychology. Religion > BF Psychology (General) > BF1-990 Psychology R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	01 Oct 2024 01:38
Last Modified:	01 Oct 2024 01:38
URII:	http://shdl.mmu.edu.my/id/eprint/13005

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