An ensemble-based feature selection framework to select risk factors of childhood obesity for policy decision making

Shi, Xi; Nikolić, Gorana; Epelde, Gorka; Arrue, Monica; Van-Dierdonck, Joseba Bidaurrazaga; Bilbao, Roberto; Moor, Bart De

doi:10.1186/s12911-021-01580-0

Cited by 9 publications

(5 citation statements)

References 39 publications

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“…The details of data sources and data types are listed in the Supplementary Material . The technical details of the data processing section have been published ( 13 ), and another published use case can be used as an example to show how the data was processed and prepared for data analytics ( 15 ).…”

Section: Methodsmentioning

confidence: 99%

System Architecture of a European Platform for Health Policy Decision Making: MIDAS

Shi

Nikolić

Fischaber

et al. 2022

Front. Public Health

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BackgroundHealthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner.MethodsThe MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources.ResultsA novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics.ConclusionsThe MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions.

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Section: Methodsmentioning

confidence: 99%

System Architecture of a European Platform for Health Policy Decision Making: MIDAS

Shi

Nikolić

Fischaber

et al. 2022

Front. Public Health

Self Cite

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“…It integrates the feature selection process with the model training process. This method considers variable interactions and is less computationally demanding than the wrapper method ( 17 ).…”

Section: Methodsmentioning

confidence: 99%

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Chen

Feng

Geng

et al. 2022

Front. Public Health

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ObjectiveThe prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.MethodsA dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.ResultsThe XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.ConclusionsXGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

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“…It integrates the feature selection process with the model training process. This method takes variable interactions into consideration and is less computationally demanding than the wrapper method 15 .…”

Section: Methodsmentioning

confidence: 99%

Evaluating the Risk of Hypertension in Residents in Primary Care in Shanghai, China with Machine Learning Algorithms

Chen

Fan

Geng

et al. 2022

Preprint

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The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China. A dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centres from 2017 to 2019 in the Pudong district of Shanghai. The XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein, BMI, elderly health self-assessment, creatinine, systolic blood pressure of the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, frequency of drinking, glucose, urea nitrogen, total cholesterol, diastolic blood pressure of the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglycerides. XGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. Integration of such a risk assessment model into primary care may improve the prevention and management of hypertension in residents.

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An ensemble-based feature selection framework to select risk factors of childhood obesity for policy decision making

Cited by 9 publications

References 39 publications

System Architecture of a European Platform for Health Policy Decision Making: MIDAS

System Architecture of a European Platform for Health Policy Decision Making: MIDAS

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Evaluating the Risk of Hypertension in Residents in Primary Care in Shanghai, China with Machine Learning Algorithms

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