Using Bayesian networks with tabu algorithm to explore factors related to chronic kidney disease with mental illness: A cross-sectional study

Xiaoli Yuan; Wenzhu Song; Yaheng Li; Qili Wang; Jianbo Qing; Wenqiang Zhi; Huimin Han; Zhiqi Qin; Hao Gong; Guohua Hou; Yafeng Li; Xiaoli Yuan; Wenzhu Song; Yaheng Li; Qili Wang; Jianbo Qing; Wenqiang Zhi; Huimin Han; Zhiqi Qin; Hao Gong; Guohua Hou; Yafeng Li

doi:10.3934/mbe.2023723

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 16194-16211. doi: 10.3934/mbe.2023723

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Using Bayesian networks with tabu algorithm to explore factors related to chronic kidney disease with mental illness: A cross-sectional study

1.
Department of Nephrology, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan 030012, China
2.
School of Public Health, Shanxi Medical University, No.56 Xinjian South Road, Taiyuan 030001, China
3.
Shanxi Provincial Key Laboratory of Kidney Disease, Taiyuan 030012, China
4.
Department of Biochemistry & Molecular Biology, Shanxi Medical University, Taiyuan, Shanxi, China
5.
Department of Nephrology, Hejin People's hospital, Yuncheng 043300, China
6.
Core Laboratory, Shanxi Provincial People's Hospital (Fifth Hospital) of Shanxi Medical University, Taiyuan 030012, China
7.
Academy of Microbial Ecology, Shanxi Medical University, Taiyuan 030012, China

Academic Editor: Wenrui Hao

Received: 02 June 2023 Revised: 26 July 2023 Accepted: 27 July 2023 Published: 10 August 2023

While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.
- Bayesian networks,
- chronic kidney disease with mental illness (KDMI),
- tabu algorithm,
- related factors,
- model construction,
- elastic net,
- propensity score matching
Citation: Xiaoli Yuan, Wenzhu Song, Yaheng Li, Qili Wang, Jianbo Qing, Wenqiang Zhi, Huimin Han, Zhiqi Qin, Hao Gong, Guohua Hou, Yafeng Li. Using Bayesian networks with tabu algorithm to explore factors related to chronic kidney disease with mental illness: A cross-sectional study[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16194-16211. doi: 10.3934/mbe.2023723

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Abstract

While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.

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