Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study

Al-Absi, Hamada R. H.; Refaee, Mahmoud Ahmed; Rehman, Atiq Ur; Islam, Mohammad Tariqul; Belhaouari, Samir Brahim; Alam, Tanvir

doi:10.1109/access.2021.3059469

Cited by 29 publications

(12 citation statements)

References 43 publications

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“…eoretical Background. In this literature, machine learning methods have played a critical role [28]. e study of exploiting patterns and experiences to improve results is known as machine learning.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

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Section: Methodsmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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“…Risk factors such as age, gender, smoking, obesity, diabetes, hypertension, low-density lipoprotein (LDL) cholesterol, and sedentary lifestyle [6] are known to be linked to CVD and some of these factors have been studied and confirmed for the Qatari population [7,8]. In fact, there exists risk score calculators for CVD such as the Framingham Risk Score [9], and ASCVD [10], which use some of the above factors to predict the 10-year risk of developing CVD.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning

Al-Absi

Islam

Refaee

et al. 2022

Sensors

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Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.

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“…Type 1 diabetes is prevalent in children with India numbering two after the USA and highest in the Southeast region [2]. World Health Organization (WHO) records that among the total deaths 2% of deaths are due to diabetes in India [3]. Diabetes and hypertension is found to be the common non-communicable disease [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

Shaikh

Parvati

Biradar

2022

IJEECS

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This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.</span>

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Risk Factors and Comorbidities Associated to Cardiovascular Disease in Qatar: A Machine Learning Based Case-Control Study

Cited by 29 publications

References 43 publications

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

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