Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Polat, Kemal

doi:10.1155/2020/2742781

Cited by 29 publications

(27 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…e automatic modeling and detection of the jersey color of players from both sides are tentatively tested in player recognition. Song et al used the ground segmentation image as a mask [8][9][10][11]. In the AdaBoost test, the performance method has been greatly improved than before.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players

Liang¹,

2021

Complexity

View full text Add to dashboard Cite

Because of its unique charm, sports video is widely welcomed by the public in today’s society. Therefore, the analysis and research of sports game video data have high practical significance and commercial value. Taking a basketball game video as an example, this paper studies the tracking feature matching of basketball players’ detection, recognition, and prediction in the game video. This paper is divided into four parts to improve the application of the interactive multimodel algorithm to track characteristic matching: moving object detection, recognition, basketball track characteristic matching, and player track characteristic matching. The main work and research results of each part are as follows: firstly, the improved K-means clustering algorithm is used to segment the golf field area; then, HSV is combined with the RGB Fujian value method to eliminate the field area; at last, straight field lines were extracted by Hough transform, and elliptical field lines were extracted by curve fitting, and the field lines were eliminated to realize the detection of moving objects. Seven normalized Hu invariant moments are used as the target features to realize the recognition of moving targets. By obtaining the feature distance between the sample and the template, the category of the sample is judged, which has a good robustness. The Kalman filter is used to match the characteristics of the basketball trajectory. Aiming at the occlusion of basketball, the least square method was used to fit the basketball trajectory, and the basketball position was predicted at the occlusion moment, which realized the occlusion trajectory matching. The matching of players’ track characteristics is realized by the CamShift algorithm based on the color model, which makes full use of players’ color information and realizes real-time performance. In order to solve the problem of occlusion between players in the track feature matching, CamShift and Kalman algorithms were used to determine the occlusion factor through the search window and then weighted Kalman and CamShift according to the occlusion degree to get the track feature matching result. The experimental results show that the detection time is greatly shortened, the memory space occupied is small, and the effect is very ideal.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players

Liang¹,

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…However, with the growth of data in volume and dimensionality, the ability of data mining algorithms to deal with mass-data becomes more important. Some classification based data mining technique, such as random forest [ 19 ] and SVM [ 20 ], has performed well for multilabel classification using knowledge-driven features. It also can reduce the complexity of the model by reducing the number of features required to train a machine learning model.…”

Section: Related Workmentioning

confidence: 99%

Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records

Wang

Huo

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Hypertension is the fifth chronic disease causing death worldwide. The early prognosis and diagnosis are critical in the hypertension care process. Inspired by human philosophy, CBR is an empirical knowledge reasoning method for early detection and intervention of hypertension by only reusing electronic health records. However, the traditional similarity calculation method often ignores the internal characteristics and potential information of medical examination data. Methods In this paper, we first calculate the weights of input attributes by a random forest algorithm. Then, the risk value of hypertension from each medical examination can be evaluated according to the input data and the attribute weights. By fitting the risk values into a risk curve of hypertension, we calculate the similarity between different community residents, and obtain the most similar case according to the similarity. Finally, the diagnosis and treatment protocol of the new case can be given. Results The experiment data comes from the medical examination of Tianqiao Community (Tongling City, Anhui Province, China) from 2012 to 2021. It contains 4143 community residents and 43,676 medical examination records. We first discuss the effect of the influence factor and the decay factor on similarity calculation. Then we evaluate the performance of the proposed FDA-CBR algorithm against the GRA-CBR algorithm and the CS-CBR algorithm. The experimental results demonstrate that the proposed algorithm is highly efficient and accurate. Conclusions The experiment results show that the proposed FDA-CBR algorithm can effectively describe the variation tendency of the risk value and always find the most similar case. The accuracy of FDA-CBR algorithm is higher than GRA-CBR algorithm and CS-CBR algorithm, increasing by 9.94 and 16.41%, respectively.

show abstract

“…A recent review identified and examined ML techniques in hypertension detection and reported a a lack of studies combining sociodemographic and clinical data with signal processing which could increase model performance ( 16 ). A previous study used ML algorithms for automatic classification of hypertension using personal features but failed to include sociodemographic data ( 17 ). Another study in India developed ML risk stratification algorithms for diabetes and hypertension using data from 2,278 patients collected by community health workers ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

Islam

Talukder

Awal

et al. 2022

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

BackgroundHypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances.MethodsWe conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors.ResultsOf the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%.ConclusionML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

show abstract

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Cited by 29 publications

References 20 publications

[Retracted] Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players

[Retracted] Using Camshift and Kalman Algorithm to Trajectory Characteristic Matching of Basketball Players

Integrating functional data analysis with case-based reasoning for hypertension prognosis and diagnosis based on real-world electronic health records

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

Contact Info

Product

Resources

About