Hypertension Classification Using Machine Learning Part II

Nasir, Nida; Oswald, Paul; Barneih, Feras; Alshaltone, Omar; Al-Shabi, Mohammad; Bonny, Talal; Shammaa, Ahmed Al

doi:10.1109/dese54285.2021.9719408

Cited by 22 publications

(14 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another reason for this contradiction is the diversity in selected the input parameters [12], where any changes in these parameters affect the performance of the model. Moreover, the usage of the machine learning could you help losing weight and improve the life quality [13][14][15][16][17][18][19][20][21][22][23]. Moreover, metaheuristic algorithms, integrated with machine learning techniques [24][25][26][27][28][29][30][31][32][33][34][35][36], can optimize the selection of input parameters for WBV studies on weight loss and quality of life improvements, overcoming challenges related to standardized protocols and diverse parameter settings, and providing more consistent and reliable outcomes [37][38][39][40][41][42][43][44][45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Studying the gender effect on apparent mass during whole-body vibration using ANN

Al-Shabi

Nawayseh

2023

Smart Biomedical and Physiological Sensor Technology XX

View full text Add to dashboard Cite

Artificial Neural Network (ANN) is a powerful tool to model a system using only the inputs and outputs of that system. In this paper, ANN is used to model the relation between the subject's gender to its performance while been excited in a whole-body vibration machine (WBV). For training the ANN, 20 male and 20 female subjects were observed during an experimental setup using a WBV at different vibration frequencies in the range of 20 to 45 Hz. The apparent mass was measured for the subjects at different frequencies. The input to the ANN includes body mass index, mass, and gender of the subjects along with and the excitation frequency. The ANN shows a good performance and extract the relationship with a performance that has a root mean squared error of the relative percentage error less than 9%.

show abstract

Section: Introductionmentioning

confidence: 99%

Studying the gender effect on apparent mass during whole-body vibration using ANN

Al-Shabi

Nawayseh

2023

Smart Biomedical and Physiological Sensor Technology XX

View full text Add to dashboard Cite

show abstract

“…Symptoms of heart disease vary, including diabetes, high blood pressure, and high cholesterol. Machine learning, which is highly used nowadays [1][2][3][4][5][6][7][8], helps detect the symptoms of heart disease for every person. Detecting heart diseases at an early stage can speed up the treatment of the disease or at least avoid reaching critical cases [9].…”

Section: Introductionmentioning

confidence: 99%

Detecting heart diseases through machine learning

Alobeidli,

Bou Nassif,

AlShabi

2023

Pattern Recognition and Tracking XXXIV

View full text Add to dashboard Cite

Heart diseases are ranked the first cause of death in the world. Australia has the highest incidence of heart disease. Approximately 125 lives every single day that's one life every 12 minutes. Heart disease describes a range of conditions that affect the heart or blood vessels and can affect anyone at any age. Also, a major concern, heart disease could cause a heart attack or stroke. Some symptoms may include chest pain, shortness of death, dizziness, fatigue, or nausea. Other serious symptoms, such as diabetes and high cholesterol, may lead to heart attacks. A healthy lifestyle, quitting smoking, and exercising are small steps to avoid heart disease. Heart diseases are easier to treat when detected early. In this paper, an effective heart disease framework is proposed. Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and Radial Basic Function (RBF) techniques for classification are used. Moreover, Feature selection is performed to minimize the features to have better accuracy. Info Gain Attribute Eval -Ranker algorithm is used for feature selection. In addition, classification techniques and feature selection algorithms are applied to the LIAC heart stat log dataset which depends on the heart diseases dataset. The result's effectiveness is described by accuracy, precision, recall, and ROC Curve.

show abstract

“…The literature addressed the prediction of face masks using a variety of techniques of artificial intelligence and machine learning, which has become popular nowadays [4][5][6][7][8][9][10][11]. Kayali et al [12] used architectures ResNet50 and NASNetMobile for training.…”

Section: Introductionmentioning

confidence: 99%

Face mask detection using machine learning

Eladham

Nassif

Al-Shabi

2023

Real-Time Image Processing and Deep Learning 2023

View full text Add to dashboard Cite

The COVID-19 epidemic forced governments to adopt worldwide lockdowns in order to limit the virus's spread. Wearing a face mask, it is said, would reduce the possibility of transmission. Due to the growing urban population, proper city management is more important than ever in the modern day to reduce the impacts of COVID-19 infection. To check the mask in public places, however, would require incredibly long lineups and delays. Therefore, it is necessary for an autonomous mask detection system to assess whether someone is wearing a face mask. On the face mask dataset, three different machine learning methods are applied to determine the likelihood of wearing a face mask. The models were assessed using a number of measures, including accuracy, recall, and ROC curve. The main objective of the study is to detect the presence of face masks using deep learning, machine learning, and image processing approaches. All three models-NB, KNN, and CNN-achieved noteworthy accuracy of more than 80%, with CNN showing the best overall performance.

show abstract

Hypertension Classification Using Machine Learning Part II

Cited by 22 publications

References 12 publications

Studying the gender effect on apparent mass during whole-body vibration using ANN

Studying the gender effect on apparent mass during whole-body vibration using ANN

Detecting heart diseases through machine learning

Face mask detection using machine learning

Contact Info

Product

Resources

About