Long-Term Hypertension Risk Prediction with ML Techniques in ELSA Database

Δρίτσας, Ηλίας; Fazakis, Nikos; Kocsis, Otilia; Fakotakis, Nikos; Μουστάκας, Κωνσταντίνος

doi:10.1007/978-3-030-92121-7_9

Cited by 18 publications

(11 citation statements)

References 33 publications

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“…Comparing the AUC scores we achieved in this study versus a meta-analysis of the literature, we find that it is close to the expected mean AUC for a hypertension risk model (5–7). Relative to other studies, the small differences in discrimination between the elastic regression and the best performing machine learning model, is similar to that found in other studies (11,21,23).…”

Section: Discussionsupporting

confidence: 88%

“…Machine learning have been used to develop hypertension risk models before, but few studies have compared their performance with low-complexity models like logistic or Cox regression (18)(19)(20). Among the studies who have, machine learning has been found to have better discrimination in some studies and worse in others (11,(21)(22)(23)(24). Calibration was assessed in only two studies applying machine learning models, and then solely by reporting a summary measure (22,25).…”

Section: Introductionmentioning

confidence: 99%

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Can machine learning improve risk prediction of incident hypertension? An internal method comparison and external validation of the Framingham risk model using HUNT Study data

Schjerven

Ingeström

Lindseth

et al. 2022

Preprint

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A recent meta-review on hypertension risk models detailed that the differences in data and study-setup have a large influence on performance, meaning model comparisons should be performed using the same study data. We compared five different machine learning algorithms and the externally developed Framingham risk model in predicting risk of incident hypertension using data from the Trøndelag Health Study. The dataset yielded n = 23722 individuals with p = 17 features recorded at baseline before follow-up 11 years later. Individuals were without hypertension, diabetes, or history of CVD at baseline. Features included clinical measurements, serum markers, and questionnaire-based information on health and lifestyle. The included modelling algorithms varied in complexity from simpler linear predictors like logistic regression to the eXtreme Gradient Boosting algorithm. The other algorithms were Random Forest, Support Vector Machines, K-Nearest Neighbor. After selecting hyperparameters using cross-validation on a training set, we evaluated the models’ performance on discrimination, calibration, and clinical usefulness on a separate testing set using bootstrapping. Although the machine learning models displayed the best performance measures on average, the improvement from a logistic regression model fitted with elastic regularization was small. The externally developed Framingham risk model performed well on discrimination, but severely overestimated risk of incident hypertension on our data. After a simple recalibration, the Framingham risk model performed as well or even better than some of the newly developed models on all measures. Using the available data, this indicates that low-complexity models may suffice for long-term risk modelling. However, more studies are needed to assess potential benefits of a more diverse feature-set. This study marks the first attempt at applying machine learning methods and evaluating their performance on discrimination, calibration, and clinical usefulness within the same study on hypertension risk modelling.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Can machine learning improve risk prediction of incident hypertension? An internal method comparison and external validation of the Framingham risk model using HUNT Study data

Schjerven

Ingeström

Lindseth

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Information and communication technologies (ICTs), and especially the fields of artificial intelligence (AI) and machine learning (ML), now play an important role in the early prediction of various diseases, such as diabetes (as a classification [18] or regression task for continuous glucose prediction [19,20]), hypertension [21], cholesterol [22], COVID-19 [23], COPD [24], CVDs [25], ALF [26], sleep disorders [27], hepatitis C [28], CKD [29], etc. In particular, the stroke will concern us in the context of this study.…”

Section: Introductionmentioning

confidence: 99%

Stroke Risk Prediction with Machine Learning Techniques

Δρίτσας

Trigka

2022

Sensors

Self Cite

138

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A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

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“…Recent advances in the fields of Artificial Intelligence (AI) and Machine Learning (ML) may provide clinicians and physicians with efficient tools for the early diagnosis of various diseases, such as Cholesterol [ 13 ], Hypertension [ 14 ], COPD [ 15 ], Continuous Glucose Monitoring [ 16 ], Short-Term Glucose prediction [ 17 ], COVID-19 [ 18 ], CVDs [ 19 ], Stroke [ 20 ], CKD [ 21 ], ALF [ 22 ], Sleep Disorders [ 23 ], Hepatitis [ 24 ] and Cancer [ 25 ]. The prediction of type 2 diabetes is the point of interest in this research work.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

Δρίτσας

Trigka

2022

Sensors

Self Cite

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Diabetes mellitus is a chronic condition characterized by a disturbance in the metabolism of carbohydrates, fats and proteins. The most characteristic disorder in all forms of diabetes is hyperglycemia, i.e., elevated blood sugar levels. The modern way of life has significantly increased the incidence of diabetes. Therefore, early diagnosis of the disease is a necessity. Machine Learning (ML) has gained great popularity among healthcare providers and physicians due to its high potential in developing efficient tools for risk prediction, prognosis, treatment and the management of various conditions. In this study, a supervised learning methodology is described that aims to create risk prediction tools with high efficiency for type 2 diabetes occurrence. A features analysis is conducted to evaluate their importance and explore their association with diabetes. These features are the most common symptoms that often develop slowly with diabetes, and they are utilized to train and test several ML models. Various ML models are evaluated in terms of the Precision, Recall, F-Measure, Accuracy and AUC metrics and compared under 10-fold cross-validation and data splitting. Both validation methods highlighted Random Forest and K-NN as the best performing models in comparison to the other models.

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Long-Term Hypertension Risk Prediction with ML Techniques in ELSA Database

Cited by 18 publications

References 33 publications

Can machine learning improve risk prediction of incident hypertension? An internal method comparison and external validation of the Framingham risk model using HUNT Study data

Can machine learning improve risk prediction of incident hypertension? An internal method comparison and external validation of the Framingham risk model using HUNT Study data

Stroke Risk Prediction with Machine Learning Techniques

Data-Driven Machine-Learning Methods for Diabetes Risk Prediction

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