Predicting hypertension using machine learning: Findings from Qatar Biobank Study

Analysis of outcomes from a geriatrician-led evidence-based falls prevention clinic: a retrospective study from Qatar

BACKGROUND

Falls are an important health concern among older persons. This study attempted to evaluate the characteristics and risk factors of falls in older persons attending a geriatric falls clinic in Qatar, with a particular emphasis on the impact of the COVID-19 epidemic.

METHODS

A retrospective analysis was performed on 265 patients aged ≥ 65 years who visited the Falls Clinic between January 1, 2019, and November 28, 2021.

RESULTS

The study population showed a significant prevalence of chronic diseases, with hypertension (94.3%) and diabetes (87.5%) being the most common diseases. A substantial percentage of people reported functional difficulties, such as falls during the recent year and injuries. Interestingly, people recruited during the pandemic used assistive devices at a far higher proportion (46.9% vs. 5.5% previously). This group also reported higher rates of dizziness (54.9% vs. 26.7%, respectively) and pain (50.2% vs. 20.0%, respectively). Participants recruited during the epidemic had significantly elevated systolic blood pressure.

CONCLUSION

This study showed an increased incidence of comorbidities and functional impairments in older adults who visited a fall prevention clinic in Qatar. These findings suggest that the pandemic may exacerbate population vulnerability.

Feature Selection for Hypertension Risk Prediction Using XGBoost on Single Nucleotide Polymorphism Data

OBJECTIVES

Hypertension, commonly known as high blood pressure, is a prevalent and serious condition affecting a significant portion of the adult population globally. It is a chronic medical issue that, if left unaddressed, can lead to severe health complications, including kidney problems, heart disease, and stroke. This study aims to develop a feature selection model using the XGBoost algorithm to identify specific single nucleotide polymorphisms (SNPs) as biomarkers for detecting hypertension risk.

METHODS

We propose using the high dimensionality of genetic variations (i.e., SNPs) to build a classifier model for prediction. In this study, SNPs were used as markers for hypertension in patients. We utilized the OpenSNP dataset, which includes 19,697 SNPs from 2,052 samples. Extreme gradient boosting (XGBoost) is an ensemble machine learning method employed here for feature selection, which incrementally adjusts weights in a series of steps.

RESULTS

The experimental results identified 292 SNPs that exhibited high performance, with an F1-score of 98.55%, precision of 98.73%, recall of 98.38%, and overall accuracy of 98%. This study provides compelling evidence that the XGBoost feature selection method outperforms other representative feature selection methods, such as genetic algorithms, analysis of variance, chi-square, and principal component analysis, in predicting hypertension risk, demonstrating its effectiveness.

CONCLUSIONS

We developed a model for predicting hypertension using the SNPs dataset. The high dimensionality of SNP data was effectively managed to identify significant features as biomarkers using the XGBoost feature selection method. The results indicate high performance in predicting the risk of hypertension.

Optimizing hypertension prediction using ensemble learning approaches

Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study investigates how well ensemble learning techniques work to increase the accuracy of HTN prediction models. Utilizing a dataset of 612 participants from Ethiopia, which includes 27 features potentially associated with HTN risk, we aimed to enhance predictive performance over traditional single-model methods. A multi-faceted feature selection approach was employed, incorporating Boruta, Lasso Regression, Forward and Backward Selection, and Random Forest feature importance, and found 13 common features that were considered for prediction. Five machine learning (ML) models such as logistic regression (LR), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), and a stacking ensemble model were trained using selected features to predict HTN. The models' performance on the testing set was evaluated using accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, SHapley Additive exPlanations (SHAP) was utilized to examine the impact of individual features on the models' predictions and identify the most important risk factors for HTN. The stacking ensemble model emerged as the most effective approach for predicting HTN risk, achieving an accuracy of 96.32%, precision of 95.48%, recall of 97.51%, F1-score of 96.48%, and an AUC of 0.971. SHAP analysis of the stacking model identified weight, drinking habits, history of hypertension, salt intake, age, diabetes, BMI, and fat intake as the most significant and interpretable risk factors for HTN. Our results demonstrate significant advancements in predictive accuracy and robustness, highlighting the potential of ensemble learning as a pivotal tool in healthcare analytics. This research contributes to ongoing efforts to optimize HTN prediction models, ultimately supporting early intervention and personalized healthcare management.

A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings

Background and Aims

The rapid expansion of artificial intelligence (AI) within worldwide healthcare systems is occurring at a significant rate. In this context, the Middle East has demonstrated distinctive characteristics in the application of AI within the healthcare sector, particularly shaped by regional policies. This study examined the outcomes resulting from the utilization of AI within healthcare systems in the Middle East.

Methods

A systematic review was conducted across several databases, including PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews in 2024. The quality assessment of the included studies was conducted using the Authority, Accuracy, Coverage, Objectivity, Date, Significance checklist. Following this, a thematic analysis was carried out on the acquired data, adhering to the Boyatzis approach.

Results

100 papers were included. The quality and bias risk of the included studies were delineated to be within an acceptable range. Multiple themes were derived from the thematic analysis including: "Prediction of diseases, their diagnosis, and outcomes," "Prediction of organizational issues and attributes," "Prediction of mental health issues and attributes," "Prediction of polypharmacy and emotional analysis of texts," "Prediction of climate change issues and attributes," and "Prediction and identification of success and satisfaction among healthcare individuals."

Conclusion

The findings emphasized AI's significant potential in addressing prevalent healthcare challenges in the Middle East, such as cancer, diabetes, and climate change. AI has the potential to overhaul the healthcare systems. The findings also highlighted the need for policymakers and administrators to develop a concrete plan to effectively integrate AI into healthcare systems.

A machine learning analysis of predictors of future hypertension in a young population

BACKGROUND

Early diagnosis of hypertension (HT) is crucial for preventing end-organ damage. This study aims to identify the risk factors for future HT in young individuals through the application of machine learning (ML) models.

METHODS

The study included individuals aged 18-40 years who had not been diagnosed with HT through ambulatory blood pressure monitoring (ABPM). These participants were monitored for hypertension diagnosis from the date of ABPM application until the date of data collection. Hypertension prediction was carried out using three distinct ML methods: Support Vector Machine, Random Forest, and Least Absolute Shrinkage and Selection Operator. The identification of variables significant for future HT was based on the outcomes of these models.

RESULTS

This study comprised 516 patients, with a mean follow-up duration of 793.4±58.6 days. Following the integration of demographic data, laboratory results, and ABPM findings into the ML models, age, high-density lipoprotein cholesterol, triglycerides, and the standard deviation of systolic blood pressure (SDsis) were identified as predictors for future HT. A logistic regression with the selected variables (age, diabetes mellitus history, HDL, triglycerides, white blood cell count, and SDsis) using the full data set gave the following log odds 0.0737 (P<0.001), 0.7146 (P<0.001), -0.0160 (P=0.071), 0.0026 (P=0.002), 0.0857 (P=0.069), and 0.0850 (P=0.005), respectively. The corresponding probability values of age, diabetes mellitus history, HDL, triglycerides, white blood cell count, and SDsis were 0.5184, 0.6714, 0.4960, 0.5006, 0.5214, and 0.5212, respectively. This indicates a unit increase in all factors, except diabetes mellitus history, increases the probability of future HT by 50%. A history of diabetes, however, increases the probability of future HT by more than two thirds. The history of diabetes mellitus emerged as the most crucial predictor of future HT across all applied methods.

CONCLUSIONS

ML methods appear to be valuable tools for predicting future HT. The widespread adoption of these methods and the refinement of more comprehensive models will lay the groundwork for future studies.

Development and validation of a nomogram model for predicting the risk of hypertension in Bangladesh

Background and objectives

Hypertension (HTN) is a leading cause of non-communicable disease in low- and middle-income countries, including Bangladesh. Thus, the objectives of this study were to investigate the associated risk factors for HTN and develop with validate a monogram model for predicting an individual's risk of HTN in Bangladesh.

Materials and methods

This study exploited the latest nationally representative cross-sectional BDHS, 2017-18 data, which consisted of 6569 participants. LASSO and logistic regression (LR) analysis were performed to reduce dimensionality of data, identify the associated risk factors, and develop a nomogram model for predicting HTN risk in the training cohort. The discrimination ability, calibration, and clinical effectiveness of the developed model were evaluated using validation cohort in terms of area under the curve (AUC), calibration plot, decision curve analysis, and clinical impact curve analysis.

Results

The combined results of the LASSO and LR analysis demonstrated that age, sex, division, physical activity, family member, smoking, body mass index, and diabetes were the associated risk factors of HTN. The nomogram model achieved good discrimination ability with AUC of 0.729 (95 % CI: 0.685-0.741) for training and AUC of 0.715 (95 % CI: 0.681-0.729)] for validation cohort and showed strong calibration effects, with good agreement between the actual and predicted probabilities (p-value = 0.231).

Conclusion

The proposed nomogram provided a good predictive performance and can be effectively utilized in clinical settings to accurately diagnose hypertensive patients who are at risk of developing severe HTN at an early stage in Bangladesh.

Next-visit prediction and prevention of hypertension using large-scale routine health checkup data

This paper proposes the use of machine learning models to predict one's risk of having hypertension in the future using their routine health checkup data of their current and past visits to a health checkup center. The large-scale and high-dimensional dataset used in this study comes from MJ Health Research Foundation in Taiwan. The training data for models is separated into 5 folds and used to train 5 models in a 5-fold cross validation manner. While predicting the results for the test set, the voted result of 5 models is used as the final prediction. Experimental results show that our models achieve 69.59% of precision, 77.90% of recall, and 73.51% of F1-score, which outperforms a baseline using only the blood pressure of visitors' last visits. Experiments also show that a visitor who performs a health checkup more often can be predicted better, and models trained with selected important factors achieve better results than those trained with Framingham risk score. We also demonstrate the possibility of using our models to suggest visitors for weight control by adding virtual visits that assume their body weight can be reduced in the near future to model input. Experimental results show that around 5.48% of the people who are with high Body Mass Index of the true positive cases are rejudged as negative, and a rising trend appears when adding more virtual visits, which may be used to suggest visitors that controlling their body weight for a longer time lead to lower probability of having hypertension in the future.

HyMNet: A Multimodal Deep Learning System for Hypertension Prediction Using Fundus Images and Cardiometabolic Risk Factors

STUDY OBJECTIVES

This study aimed to develop a multimodal deep learning (MMDL) system called HyMNet, integrating fundus images and cardiometabolic factors (age and sex) to enhance hypertension (HTN) detection.

METHODS

HyMNet employed RETFound, a model pretrained on 1.6 million retinal images, for the fundus data, in conjunction with a fully connected neural network for age and sex. The two pathways were jointly trained by joining their feature vectors into a fusion network. The system was trained on 5016 retinal images from 1243 individuals provided by the Saudi Ministry of National Guard Health Affairs. The influence of diabetes on HTN detection was also assessed.

RESULTS

HyMNet surpassed the unimodal system, achieving an F1 score of 0.771 compared to 0.745 for the unimodal model. For diabetic patients, the F1 score was 0.796, while it was 0.466 for non-diabetic patients.

CONCLUSIONS

HyMNet exhibited superior performance relative to unimodal approaches, with an F1 score of 0.771 for HyMNet compared to 0.752 for models trained on demographic data alone, underscoring the advantages of MMDL systems in HTN detection. The findings indicate that diabetes significantly impacts HTN prediction, enhancing detection accuracy among diabetic patients. Utilizing MMDL with diverse data sources could improve clinical applicability and generalization.

A Study on Prevalence and Factors Affecting Hypertension in an Iranian Population: Results from the Fasa Cohort Study

Background

In recent years, hypertension has been one of the most important noncommunicable diseases worldwide. In this context, identifying the predictors of this disease can help health policymakers to reduce its burden. This study aimed to identify some of the most important influential factors of hypertension and present a model to predict this disease in the data from a large sample cohort study.

Methods

The data set included 10,138 people from the baseline phase of the Fasa cohort study during 2014 and 2016. The main outcome under study was having hypertension in the baseline phase of the study according to self-reports or medical examinations. To identify the related factors of hypertension, logistic regression, classification tree, and random forest models were utilized. Statistical analyses were performed in R.

Results

Among the 10,138 people examined, 2819 (27.8%) had hypertension. In the initial screening, 39 variables were regarded as potential indicators of hypertension. After preliminary analysis, 11 variables were recognized as important predictors based on the importance index: history of cardiovascular disease, cardiac disease, waist circumference to height ratio, body mass index, sex, hypertension in a first-degree relative, weight, fatty liver, cardiac disease in a first-degree relative, diabetes in a first-degree relative, and energy intake. The area under the receiving operating characteristic (ROC) curve for predicting hypertension using logistic regression, classification tree, and random forest models was about 72.8%, 73%, and 87.6%, respectively. Also, the accuracy of these models was 65.2%, 67.4% and 77.8%, respectively.

Conclusion

In general, our findings showed that machine learning-based approaches, such as random forest models, outperformed classical methods, such as logistic regression in predicting hypertension. Regarding the rather high prevalence of hypertension in the population under study, there is an urgent need to pay more attention to its indicators for early diagnosis of the patients and reducing the burden of this silent disease in our country.

Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional study

Objective

To develop a predictive model for undiagnosed hypertension (UHTN) in older adults based on five modifiable factors [eating behaviors, emotion, exercise, stopping smoking, and stopping drinking alcohol (3E2S) using machine learning (ML) algorithms.

Methods

The supervised ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB)] with SHapley Additive exPlanations (SHAP) prioritization and conventional statistics (χ2 and binary logistic regression) were employed to predict UHTN from 5,288 health records of older adults from ten primary care hospitals in Thailand.

Results

The χ2 analyses showed that age and eating behavior were the predicting features of UHTN occurrence. The binary logistic regression revealed that taking food supplements/vitamins, using seasoning powder, and eating bean products were related to normotensive and hypertensive classifications. The RF, XGB, and SVM accuracy were 0.90, 0.89, and 0.57, respectively. The SHAP identified the importance of salt intake and food/vitamin supplements. Vitamin B6, B12, and selenium in the UHTN were lower than in the normotensive group.

Conclusion

ML indicates that salt intake, soybean consumption, and food/vitamin supplements are primary factors for UHTN classification in older adults.

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

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.

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up

BACKGROUND

Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area.

METHODS

This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35-70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP).

RESULTS

Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth.

CONCLUSION

ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.

The Evaluation of Ordinal Regression Model's Performance Through the Implementation of Multilayer Feed-Forward Neural Network: A Case Study of Hypertension

Background Hypertension, or high blood pressure, is a common medical condition that affects a significant portion of the global population. It is a major risk factor for cardiovascular diseases (CVD), stroke, and kidney disorders. Objective The objective of this study is to create and validate a model that combines bootstrapping, ordered logistic regression, and multilayer feed-forward neural networks (MLFFNN) to identify and analyze the factors associated with hypertension patients who also have dyslipidemia. Material and methods A total of 33 participants were enrolled from the Hospital Universiti Sains Malaysia (USM) for this study. In this study, advanced computational statistical modeling techniques were utilized to examine the relationship between hypertension status and several potential predictors. The RStudio (Posit, Boston, MA) software and syntax were implemented to establish the relationship between hypertension status and the predictors. Results The statistical analysis showed that the developed methodology demonstrates good model fitting through the value of predicted mean square error (MSE), mean absolute deviance (MAD), and accuracy. To evaluate model fitting, the data in this study was divided into distinct training and testing datasets. The findings revealed that the results strongly support the superior predictive capability of the hybrid model technique. In this case, five variables are considered: marital status, smoking status, systolic blood pressure, fasting blood sugar levels, and high-density lipoprotein levels. It is important to note that all of them affect the hazard ratio: marital status (β1, -17.12343343; p < 0.25), smoking status (β2, 1.86069121; p < 0.25), systolic blood pressure (β3, 0.05037332; p < 0.25), fasting blood sugar (β4, -0.53880322; p < 0.25), and high-density lipoprotein (β5, 5.38065556; p < 0.25). Conclusion This research aims to develop and extensively evaluate the hybrid approach. The statistical methods employed in this study using R language show that regression modeling surpasses R-squared values in predicting the mean square error. The study's conclusion provides strong evidence for the superiority of the hybrid model technique.

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

BACKGROUND

As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area.

METHODS

This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used.

RESULTS

A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data.

CONCLUSION

The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.

Uncovering the factors that affect earthquake insurance uptake using supervised machine learning

The escalating threat of natural disasters to public safety worldwide underlines the crucial role of effective environmental risk management tools, such as insurance. This is particularly evident in the case of earthquakes that occurred in Oklahoma between 2011 and 2020, which were linked to wastewater injection, underscoring the need for earthquake insurance. In this regard, from a survey of 812 respondents in Oklahoma, USA, we used supervised machine learning techniques (i.e., logit, ridge, least absolute shrinkage and selection operator (LASSO), decision tree, and random forest classifiers) to identify the factors that influence earthquake insurance uptake and to predict individuals who would acquire earthquake insurance. Our findings reveal that influential factors that affect earthquake insurance uptake include demographic factors such as older age, male gender, race, and ethnicity. These were found to significantly influence the decision to purchase earthquake insurance. Additionally, individuals residing in rental properties were less likely to purchase earthquake insurance, while longer residency in Oklahoma had a positive influence. Past experience of earthquakes was also found to positively influence the decision to purchase earthquake insurance. Both decision trees and random forests demonstrated good predictive capabilities for identifying earthquake insurance uptake. Notably, random forests exhibited higher precision and robustness, emerging as an encouraging choice for earthquake insurance modeling and other classification problems. Empirically, we highlight the importance of insurance as an environmental risk management tool and emphasize the need for awareness and education on earthquake insurance as well as the use of supervised machine learning algorithms for classification problems.

Diabetes and hypertension MobileHealth systems: a review of general challenges and advancements

Mobile health (mHealth) systems are sipping into more and more healthcare functions with self-management being the foremost modus operandi. However, there has been challenges. This study explores challenges with mHealth self-management of diabetes and hypertension, two of the most comorbid chronic diseases. Existing literature present the challenges in fragments, certain subsets of the challenges at a time. Nevertheless, feedback from patient/users in extant literature depict very variegated concerns that are also interdependent. This work pursues provision of an encyclopedic, but not redundant, view of the challenges with mHealth systems for self-management of diabetes and hypertension.

Furthermore, the work identifies machine learning (ML) and self-management approaches as potential drivers of potency of diabetes and hypertension mobile health systems. The nexus between ML and diabetes and hypertension mHealth systems was found to be under-explored. For ML contributions to management of diabetes, we found that machine learning has been applied most to diabetes prediction followed by diagnosis, with therapy in distant third. For diabetes therapy research, only physical and dietary therapy were emphasized in reviewed literature. The four most considered performance metrics were accuracy, ROC-AUC, sensitivity, and specificity. Random forest was the best performing algorithm across all metrics, for all purposes covered in the literature. For hypertension, in descending order, hypertension prediction, prediction of risk factors, and prediction of prehypertension were most considered areas of hypertension management witnessing application of machine learning. SVM averaged best ML algorithm in accuracy and sensitivity, while random forest averaged best performing in specificity and ROC-AUC.

Healthcare data quality assessment for improving the quality of the Korea Biobank Network

Numerous studies make extensive use of healthcare data, including human materials and clinical information, and acknowledge its significance. However, limitations in data collection methods can impact the quality of healthcare data obtained from multiple institutions. In order to secure high-quality data related to human materials, research focused on data quality is necessary. This study validated the quality of data collected in 2020 from 16 institutions constituting the Korea Biobank Network using 104 validation rules. The validation rules were developed based on the DQ4HEALTH model and were divided into four dimensions: completeness, validity, accuracy, and uniqueness. Korea Biobank Network collects and manages human materials and clinical information from multiple biobanks, and is in the process of developing a common data model for data integration. The results of the data quality verification revealed an error rate of 0.74%. Furthermore, an analysis of the data from each institution was performed to examine the relationship between the institution's characteristics and error count. The results from a chi-square test indicated that there was an independent correlation between each institution and its error count. To confirm this correlation between error counts and the characteristics of each institution, a correlation analysis was conducted. The results, shown in a graph, revealed the relationship between factors that had high correlation coefficients and the error count. The findings suggest that the data quality was impacted by biases in the evaluation system, including the institution's IT environment, infrastructure, and the number of collected samples. These results highlight the need to consider the scalability of research quality when evaluating clinical epidemiological information linked to human materials in future validation studies of data quality.

Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017-March 2020

Background

Hypertension is a major public health problem, and its resulting other cardiovascular diseases are the leading cause of death worldwide. In this study, we constructed a convenient and high-performance hypertension risk prediction model to assist in clinical diagnosis and explore other important influencing factors.

Methods

We included 8,073 people from NHANES (2017-March 2020), using their 120 features to form the original dataset. After data pre-processing, we removed several redundant features through LASSO regression and correlation analysis. Thirteen commonly used machine learning methods were used to construct prediction models, and then, the methods with better performance were coupled with recursive feature elimination to determine the optimal feature subset. After data balancing through SMOTE, we integrated these better-performing learners to construct a fusion model based for predicting hypertension risk on stacking strategy. In addition, to explore the relationship between serum ferritin and the risk of hypertension, we performed a univariate analysis and divided it into four level groups (Q1 to Q4) by quartiles, with the lowest level group (Q1) as the reference, and performed multiple logistic regression analysis and trend analysis.

Results

The optimal feature subsets were: age, BMI, waist, SBP, DBP, Cre, UACR, serum ferritin, HbA1C, and doctors recommend reducing salt intake. Compared to other machine learning models, the constructed fusion model showed better predictive performance with precision, accuracy, recall, F1 value and AUC of 0.871, 0.873, 0.871, 0.869 and 0.966, respectively. For the analysis of the relationship between serum ferritin and hypertension, after controlling for all co-variates, OR and 95% CI from Q2 to Q4, compared to Q1, were 1.396 (1.176-1.658), 1.499 (1.254-1.791), and 1.645 (1.360-1.989), respectively, with P < 0.01 and P for trend <0.001.

Conclusion

The hypertension risk prediction model developed in this study is efficient in predicting hypertension with only 10 low-cost and easily accessible features, which is cost-effective in assisting clinical diagnosis. We also found a trend correlation between serum ferritin levels and the risk of hypertension.

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

BACKGROUND AND OBJECTIVES

Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia.

MATERIALS AND METHODS

The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN.

RESULTS

The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN.

CONCLUSIONS

The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.

Survey and Evaluation of Hypertension Machine Learning Research

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension-related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real-time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Whole-exome sequencing for screening noise-induced hearing loss susceptibility genes

BACKGROUND

High-throughput sequencing of genes indicating susceptibility to noise-induced hearing loss has not previously been reported.

AIMS/OBJECTIVES

To identify and analyze genes associated with susceptibility to noise-induced hearing loss (NIHL) and characterize differences in susceptibility to hearing loss by genotype.

MATERIAL AND METHODS

Pure tone audiometry tests were performed on 113 workers exposed to high-intensity noise. Whole-exome sequencing (WES) was conducted and NIHL susceptibility genes screened for training unsupervised and supervised machine learning models. Immunofluorescence staining of mouse cochlea was used to observe patterns of NIHL susceptibility gene expression.

RESULTS

Participants were divided into a NIHL and a control group, according to the results of audiometry tests. Seventy-three possible NIHL susceptibility genes were input into the machine learning model. Two subgroups of NIHL could be distinguished by unsupervised machine learning and the classification was evaluated by the supervised machine learning algorithm. The VWF gene had the highest mutation frequency in the NIHL group and was expressed mainly in the spiral ligament.

CONCLUSIONS AND SIGNIFICANCE

NIHL susceptibility genes were screened and NIHL subgroups could be distinguished. VWF may be a novel NIHL susceptibility gene.

Body composition predicts hypertension using machine learning methods: a cohort study

We used machine learning methods to investigate if body composition indices predict hypertension. Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting, and Stacking to classify the investigated cases and find the most relevant features to hypertension. FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. AutoMLP, stacking and voting methods had the best performance for hypertension prediction achieving an accuracy rate of 90%, 84% and 83%, respectively. By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with acceptable accuracy.

Affecting factors for abdominal incisional tension in surgery of dogs and cats

OBJECTIVE

Proper assessment of intraoperative abdominal incisional tension helps to select the appropriate sutures and suture method. Wound tension is usually thought to be associated with wound size, but few relevant articles have been reported. The aim of this study was to investigate the core factors influencing abdominal incisional tension and construct regression equations to judge incisional tension in clinical surgery.

METHODS

Medical records were collected from clinical surgical cases at the Teaching Animal Hospital of Nanjing Agricultural University from March 2022 to June 2022. The data collected mainly included the body weight, and the incisional length, margin, and tension. The core factors affecting abdominal wall incisional tension were screened by correlation analysis, random forest analysis, and multiple linear regression analysis.

RESULTS

Although correlation analysis showed that multiple same and deep layer abdominal incision parameters and body weight were significantly correlated with abdominal incisional tension. However, the same layer of abdominal incisional margin had the largest correlation coefficient. In random forest models, the abdominal incisional margin had the main contribution to the prediction of the same layer's abdominal incisional tension. In the multiple linear regression model, all incisional tension could be predicted by the same layer of abdominal incisional margin as the only independent variable, except for canine muscle and subcutaneous. The canine muscle and subcutaneous incisional tension were binary regressions with the same layer's abdominal incision margin and body weight.

CONCLUSION

The same layer's abdominal incisional margin is the core factor positively related to the abdominal incisional tension intraoperatively.

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this study is to develop a deep learning algorithm for the classification and prediction of hypertension with BP-related factors based on the Korean Genome and Epidemiology Study-Ansan and Ansung baseline survey. We also investigated whether energy intake adjustment is adequate for deep learning algorithms. We constructed a deep neural network (DNN) in which the number of hidden layers and the number of nodes in each hidden layer are experimentally selected, and we trained the DNN to diagnose hypertension using the dataset while varying the energy intake adjustment method in four ways. For comparison, we trained a decision tree in the same way. Experimental results showed that the DNN performs better than the decision tree in all aspects, such as having higher sensitivity, specificity, F1-score, and accuracy. In addition, we found that unlike general machine learning algorithms, including the decision tree, the DNNs perform best when energy intake is not adjusted. The result indicates that energy intake adjustment is not required when using a deep learning algorithm to classify and predict hypertension with BP-related factors.

Machine Learning for Hypertension Prediction: a Systematic Review

PURPOSE OF REVIEW

To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject.

RECENT FINDINGS

The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.

Early prediction of diabetes by applying data mining techniques: A retrospective cohort study

BACKGROUND

Saudi Arabia ranks 7th globally in terms of diabetes prevalence, and its prevalence is expected to reach 45.36% by 2030. The cost of diabetes is expected to increase to 27 billion Saudi riyals in cases where undiagnosed individuals are also documented. Prevention and early detection can effectively address these challenges.

OBJECTIVE

To improve healthcare services and assist in building predictive models to estimate the probability of diabetes in patients.

METHODS

A chart review, which was a retrospective cohort study, was conducted at the National Guard Health Affairs in Riyadh, Saudi Arabia. Data were collected from 5 hospitals using National Guard Health Affairs databases. We used 38 attributes of 21431 patients between 2015 and 2019. The following phases were performed: (1) data collection, (2) data preparation, (3) data mining and model building, and (4) model evaluation and validation. Subsequently, 6 algorithms were compared with and without the synthetic minority oversampling technique.

RESULTS

The highest performance was found in the Bayesian network, which had an area under the curve of 0.75 and 0.71.

CONCLUSION

Although the results were acceptable, they could be improved. In this context, missing data owing to technical issues played a major role in affecting the performance of our model. Nevertheless, the model could be used in prevention, health monitoring programs, and as an automated mass population screening tool without the need for extra costs compared to traditional methods.

Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models

DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood samples of 50 elderly individuals were collected three times at three visits for DNA methylome profiling. Participants who had a history of hypertension and/or current high BP measure were considered to have high BP. The whole dataset was randomly divided to conduct a nested five-group cross-validation for prediction performance. Data in each outer training set were independently normalized using a min–max scaler, reduced dimensionality using principal component analysis, then fed into three predictive algorithms. Of the three ML algorithms, DL achieved the best performance (AUPRC = 0.65, AUROC = 0.73, accuracy = 0.69, and F1-score = 0.73). To confirm the reliability of using DNA methylome as a biomarker for high BP, we constructed mixed-effects models and found that 61,694 methylation sites located in 15,523 intragenic regions and 16,754 intergenic regions were significantly associated with BP measures. Our proposed models pioneered the methodology of applying ML and DNA methylome data for early detection of high BP in clinical practices.

Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure

Machine learning is increasingly being used to predict clinical outcomes. Most comparisons of different methods have been based on empirical analyses in specific datasets. We used Monte Carlo simulations to determine when machine learning methods perform better than statistical learning methods in a specific setting. We evaluated six learning methods: stochastic gradient boosting machines using trees as the base learners, random forests, artificial neural networks, the lasso, ridge regression, and linear regression estimated using ordinary least squares (OLS). Our simulations were informed by empirical analyses in patients with acute myocardial infarction (AMI) and congestive heart failure (CHF) and used six data-generating processes, each based on one of the six learning methods, to simulate continuous outcomes in the derivation and validation samples. The outcome was systolic blood pressure at hospital discharge, a continuous outcome. We applied the six learning methods in each of the simulated derivation samples and evaluated performance in the simulated validation samples. The primary observation was that neural networks tended to result in estimates with worse predictive accuracy than the other five methods in both disease samples and across all six data-generating processes. Boosted trees and OLS regression tended to perform well across a range of scenarios.

Predicting risks of low birth weight in Bangladesh with machine learning

Background and objective

Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms.

Materials and methods

Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017–18, which had 2351 respondents. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve.

Results

The average percentage of low birth weight in Bangladesh was 16.2%. The respondent’s region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve.

Conclusions

Logistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study’s findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh.

A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

Introduction: In biobanks, participants' biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI's role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included "biobanks", "AI", "machine learning", and "deep learning", as well as combinations such as "biobanks with AI", "deep learning in the biobanking field", and "recent advances in biobanking". Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle-Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study's goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

Routine blood biomarkers for the detection of multiple myeloma using machine learning

INTRODUCTION

Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available.

METHODS

Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms.

RESULTS

The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set.

CONCLUSIONS

Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

Evaluation of Risk Factors Associated With Antihypertensive Treatment Success Employing Data Mining Techniques

Objective:

This study aimed to evaluate the effects of potential risk factors on antihypertensive treatment success.

Methods:

Patients with hypertension who were treated with antihypertensive medications were included in this study. Data from the last visit were analyzed retrospectively for each patient. To evaluate the predictive models for antihypertensive treatment success, data mining algorithms (logistic regression, decision tree, random forest, and artificial neural network) using 5-fold cross-validation were applied. Additionally, study parameters between patients with controlled and uncontrolled hypertension were statistically compared and multiple regression analyses were conducted for secondary endpoints.

Results:

The data of 592 patients were included in the analysis. The overall blood pressure control rate was 44%. The performance of random forest algorithm (accuracy = 97.46%, precision = 97.08%, F1 score = 97.04%) was slightly higher than other data mining algorithms including logistic regression (accuracy = 87.31%, precision = 86.21%, F1 score = 85.74%), decision tree (accuracy = 76.94%, precision = 70.64%, F1 score = 76.54%), and artificial neural network (accuracy = 86.47%, precision = 83.85%, F1 score = 84.86%). The top 5 important categorical variables (predictive correlation value) contributed the most to the prediction of antihypertensive treatment success were use of calcium channel blocker (−0.18), number of antihypertensive medications (0.18), female gender (0.10), alcohol use (−0.09) and attendance at regular follow up visits (0.09), respectively. The top 5 numerical variables contributed the most to the prediction of antihypertensive treatment success were blood urea nitrogen (−0.12), glucose (−0.12), hemoglobin A1c (−0.12), uric acid (−0.09) and creatinine (−0.07), respectively. According to the decision tree model; age, gender, regular attendance at follow-up visits, and diabetes status were identified as the most critical patterns for stratifying the patients.

Conclusion:

Data mining algorithms have the potential to produce predictive models for screening the antihypertensive treatment success. Further research on larger populations and longitudinal datasets are required to improve the models.

The Use of Machine Learning for the Care of Hypertension and Heart Failure

Machine learning (ML) is a branch of artificial intelligence that combines computer science, statistics, and decision theory to learn complex patterns from voluminous data. In the last decade, accumulating evidence has shown the utility of ML for prediction, diagnosis, and classification of hypertension and heart failure (HF). In addition, ML-enabled image analysis has potential value in assessing cardiac structure and function in an accurate, scalable, and efficient way. Considering the high burden of hypertension and HF in China and worldwide, ML may help address these challenges from different aspects. Indeed, prior studies have shown that ML can enhance each stage of patient care, from research and development, to daily clinical practice and population health. Through reviewing the published literature, the aims of the current systemic review are to summarize the utilities of ML for the care of those with hypertension and HF.

Influence of dietary total antioxidant capacity on the association between smoking and hypertension in Brazilian graduates (CUME project)

BACKGROUND AND AIMS

Hypertension (HTN) is a chronic non-communicable disease influenced by non-modifiable risk factors, such as sex and age, as well as modifiable risk factors such as lifestyle, including diet and smoking. Moreover, diet quality among smokers is worse than that of non-smokers, mainly in terms of antioxidant content. Thus, the current study aimed to investigate whether dietary total antioxidant capacity (dTAC) influences the association between smoking and HTN.

METHODS AND RESULTS

This cross-sectional study included 4303 graduates (69.35% women) from the Cohort of Minas Gerais Universities (CUME) project. An online food frequency questionnaire was administered to participants, and dTAC was estimated using the ferric reducing antioxidant power method. In the questionnaires, individuals reported smoking status, systolic and diastolic blood pressure values, previous HTN diagnosis, and use of antihypertensive drugs. Logistic regression models were used to estimate the odds ratio and 95% confidence interval between smoking and HTN, stratified by the median dTAC. Current and former smokers had higher dTAC values despite their lower fruit intake. Moreover, coffee was the main contributor to dTAC among them. Smoking was associated with a higher likelihood of HTN, mainly among individuals with a higher dTAC. However, after exclusion of coffee antioxidant capacity, there was an association between only smoking and HTN in individuals with lower dTAC.

CONCLUSIONS

The controversial association between higher dTAC and HTN can result from high coffee intake. Higher dTAC without coffee intake may mitigate the association between smoking and HTN in this population.