Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia

Islam, Md. Merajul; Alam, Md. Jahangir; Maniruzzaman, Md; Ahmed, N. A. M. Faisal; Ali, Md Sujan; Rahman, Md. Jahanur; Roy, Dulal Chandra

doi:10.1371/journal.pone.0289613

Cited by 12 publications

(2 citation statements)

References 70 publications

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“…The Neural Network consists of interconnected processing nodes organised in three layers: input, hidden, and output layers (Supplementary Figure S4). The input layer is connected to the hidden layer with updated weight, which is then connected to the output layer (57). Overfitting and underfitting are two common problems in machine learning that can have a major impact on the performance and generalization ability of models (58).…”

Section: Machine Learning Model Constructionmentioning

confidence: 99%

Using machine learning to evaluate the value of genetic liabilities in classification of hypertension within the UK Biobank

MacCarthy,

Pazoki

2024

Preprint

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Background and objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension prediction model and investigate the potential influence of genetic liability for risk factors linked to CVD on hypertension risk using Random Forest (RF) and Neural Network (NN). Materials and methods: The study included 244,718 participants of European ancestry. Genetic liabilities were constructed using previously identified genetic variants associated with various cardiovascular risk factors through genome-wide association studies (GWAS). The sample was randomly split into training and testing sets at a 70:30 ratio. We used RF and NN techniques to develop prediction models in the training set with or without feature selection. We evaluated the models' discrimination performance using the area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using RF and NN methods, respectively. Adding genetic liabilities resulted in a modest improvement in the AUC for RF but not for NN. The best prediction model was achieved using RF (AUC =0.71, Spiegelhalter z score= 0.10, P-value= 0.92, calibration slope=0.99) constructed in stage two. Conclusion: Incorporating genetic factors in the model may provide a modest incremental value for hypertension prediction beyond baseline characteristics. Our study highlighted the importance of genetic liabilities for both total cholesterol and LDL within the same prediction model adds value to the classification of hypertension.

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Section: Machine Learning Model Constructionmentioning

confidence: 99%

Using machine learning to evaluate the value of genetic liabilities in classification of hypertension within the UK Biobank

MacCarthy,

Pazoki

2024

Preprint

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“…The neural network consists of interconnected processing nodes organized in three layers: input, hidden, and output layers (Supplementary Figure S1). The input layer is connected to the hidden layer with updated weight, which is then connected to the output layer [68]. In the construction of our neural network models, the optimal number of hidden layers was identified as 5 hidden layers.…”

Section: Machine Learning Model Constructionmentioning

confidence: 99%

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

MacCarthy,

Pazoki

2024

JCM

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Background and Objective: Hypertension increases the risk of cardiovascular diseases (CVD) such as stroke, heart attack, heart failure, and kidney disease, contributing to global disease burden and premature mortality. Previous studies have utilized statistical and machine learning techniques to develop hypertension prediction models. Only a few have included genetic liabilities and evaluated their predictive values. This study aimed to develop an effective hypertension classification model and investigate the potential influence of genetic liability for multiple risk factors linked to CVD on hypertension risk using the random forest and the neural network. Materials and Methods: The study involved 244,718 European participants, who were divided into training and testing sets. Genetic liabilities were constructed using genetic variants associated with CVD risk factors obtained from genome-wide association studies (GWAS). Various combinations of machine learning models before and after feature selection were tested to develop the best classification model. The models were evaluated using area under the curve (AUC), calibration, and net reclassification improvement in the testing set. Results: The models without genetic liabilities achieved AUCs of 0.70 and 0.72 using the random forest and the neural network methods, respectively. Adding genetic liabilities improved the AUC for the random forest but not for the neural network. The best classification model was achieved when feature selection and classification were performed using random forest (AUC = 0.71, Spiegelhalter z score = 0.10, p-value = 0.92, calibration slope = 0.99). This model included genetic liabilities for total cholesterol and low-density lipoprotein (LDL). Conclusions: The study highlighted that incorporating genetic liabilities for lipids in a machine learning model may provide incremental value for hypertension classification beyond baseline characteristics.

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Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare

Yuan,

Yu,

Xie

et al. 2024

Medicine Advances

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Machine learning (ML) has achieved substantial success in performing healthcare tasks in which the configuration of every part of the ML pipeline relies heavily on technical knowledge. To help professionals with borderline expertise to better use ML techniques, Automated ML (AutoML) has emerged as a prospective solution. However, most models generated by AutoML are black boxes that are challenging to comprehend and deploy in healthcare settings. We conducted a systematic review to examine AutoML with interpretation systems for healthcare. We searched four databases (MEDLINE, EMBASE, Web of Science, and Scopus) complemented with seven prestigious ML conferences (AAAI, ACL, ICLR, ICML, IJCAI, KDD, and NeurIPS) that reported AutoML with interpretation for healthcare before September 1, 2023. We included 118 articles related to AutoML with interpretation in healthcare. First, we illustrated AutoML techniques used in the included publications, including automated data preparation, automated feature engineering, and automated model development, accompanied by a real‐world case study to demonstrate the advantages of AutoML over classic ML. Then, we summarized interpretation methods: feature interaction and importance, data dimensionality reduction, intrinsically interpretable models, and knowledge distillation and rule extraction. Finally, we detailed how AutoML with interpretation has been used for six major data types: image, free text, tabular data, signal, genomic sequences, and multi‐modality. To some extent, AutoML with interpretation provides effortless development and improves users' trust in ML in healthcare settings. In future studies, researchers should explore automated data preparation, seamless integration of automation and interpretation, compatibility with multi‐modality, and utilization of foundation models.

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Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia

Cited by 12 publications

References 70 publications

Using machine learning to evaluate the value of genetic liabilities in classification of hypertension within the UK Biobank

Using machine learning to evaluate the value of genetic liabilities in classification of hypertension within the UK Biobank

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare

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