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A Supervised Multi-tree XGBoost Model for an Earlier COVID-19 Diagnosis Based on Clinical Symptoms
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Konferans Bildirileri
Kaynak :
IEEE Xplore
Yazar(lar) :
Asif Hassan Syed , Tabrej Khan
Konu :
Computing and Processing
Yıl :
2022
Asif Hassan Syed
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Efficient screening of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) enables quick and efficient diagnosis of SARS-CoV-2 and can mitigate the burden on healthcare systems. The aim was to assist the medical team globally in triaging incoming patients, especially in countries with limited healthcare infrastructure. In this context, the features with imminent infection risk (Test Indication, Fever, and Headache) were obtained using a multi-tree XGBoost algorithm. Based on their feature importance, the top three clinically relevant earlier clinical symptoms (attributes) were employed to create a Multi-tree XGBoost-based model for an earlier prediction of SARS-CoV-2. Overall, our Multi-tree XGBoost model predicted SARS-CoV-2 infection status with a high F1-score (0.9920 \n$\\pm \\boldsymbol{0.008)}$\n and AUC value (0. 9974 ± 0.0026) only by assessing the primary three clinical symptoms related to COVID-19 infection. Thus our multi-tree XGBoost - based model suggests a simple and accurate method for earlier detection of SARS-CoV-2 cases and initiating proper treatment protocol for SARS-CoV-2 positive patients. Therefore, we can conclude that our model will allow the health organizations to potentially reduce the infection rate and mortality in masses with COVID-19 infection and fatality due to SARS-CoV-2.
DOI : 10.1109/cdma54072.2022.00041
Anahtar Kelimeler : COVID-19, Protocols, Medical services, Organizations, Predictive models, Prediction algorithms, Data models, SARS-CoV-2, Feature selection, multi-tree XGBoost, Clinical Symptoms, Machine Learning model, Earlier SARS-CoV-2 diagnosis
2
Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study
Dergi Makalesi

Dergi Makalesi
Kaynak :
IEEE Xplore
Dergi:
IEEE Access
Yazar(lar) :
Asif Hassan Syed , Tabrej Khan
Konu :
Aerospace, Bioengineering, Communication, Networking and Broadcast Technologies, Components, Circuits, Devices and Systems, Computing and Processing, Engineered Materials, Dielectrics and Plasmas, Engineering Profession, Fields, Waves and Electromagnetics, General Topics for Engineers, Geoscience, Nuclear Engineering, Photonics and Electrooptics, Power, Energy and Industry Applications, Robotics and Control Systems, Signal Processing and Analysis, Transportation
Yıl :
2020
Asif Hassan Syed
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
Earlier detection of individuals at the highest risk of developing diabetes is crucial to avoid the disease's prevalence and progression. Therefore, we aim to build a data-driven predictive application for screening subjects at a high risk of developing Type 2 Diabetes mellitus (T2DM) in the western region of Saudi Arabia. In this context, we designed and implemented a questionnaire-based cross-sectional study using conventional diabetes risk factors for studying the prevalence and the association between the outcomes and exposure (s). We used the Chi-Squared test and binary logistic regression to analyze and screen the most significant diabetes risk factor for T2DM risk prediction. Synthetic Minority Over-sampling Technique (SMOTE), a class-balancer, was used to balance the cross-sectional data. We used the balanced class data to screen the best performing classification algorithm to classify patients at high risk of diabetes with a higher F1 Score. The best performing classifier's hyper-parameters were further tuned using 10-fold cross-validation for achieving an improved F1 Score. Additionally, we validated our proposed model with the existing models built using the National Health and Nutrition Examination Survey (NHANES) dataset and Pima Indian Diabetes (PID) dataset. The results of the Chi-squared test and binary logistic regression showed that the exposures, namely Smoking, Healthy diet, Blood-Pressure (BP), Body Mass Index (BMI), Gender, and Region, contributed significantly (p <; 0.05) to the prediction of the Response variable (subjects at high risk of diabetes). The tuned two-class Decision Forest (DF) model showed better performance with an average F1score of 0.8453 ± 0.0268. Moreover, the DF based model adapted reasonably well in different diabetes dataset. An Application Programming Interface (API) of the tuned DF model was implemented and deployed as a web service at https://type2-diabetes-risk-predictor.herokuapp.com, and the implementation codes are available at https://github.com/SAH-ML/T2DM-Risk-Predictor.
DOI : 10.1109/access.2020.3035026
Anahtar Kelimeler : Adaptation models, Web services, Forestry, Predictive models, Diabetes, Indexes, Logistics, application program interfaces, blood pressure measurement, diseases, learning (artificial intelligence), medical diagnostic computing, patient diagnosis, pattern classification, regression analysis, risk management, statistical testing, smoking, healthy diet, blood pressure, body mass index, gender, National Health and Nutrition Examination Survey dataset, patient classification, retrospective cross-sectional study, diabetes risk factor, application programming interface, diabetes dataset, two-class decision forest, chi-squared test, Pima Indian Diabetes dataset, F1 score, cross-validation, classifier, balanced class data, cross-sectional data, class-balancer, synthetic minority over-sampling technique, T2DM risk prediction, binary logistic regression, questionnaire-based cross-sectional study, type 2 diabetes mellitus, data-driven predictive application, disease, Saudi Arabia, machine learning, Application, cross-sectional study, predictive model, statistical techniques/model, type 2 diabetes mellitus (T2DM)
ISSN: 2169-3536
Cilt: 8
Sayfa: 199539-199561
3
An Ensemble-Learning Based Application to Predict the Earlier Stages of Alzheimer’s Disease (AD)
Dergi Makalesi

Dergi Makalesi
Kaynak :
IEEE Xplore
Dergi:
IEEE Access
Yazar(lar) :
Asif Hassan Syed , Tabrej Khan , Atif Hassan , Nashwan A. Alromema , Muhammad Binsawad , Alhuseen Omar Alsayed
Konu :
Aerospace, Bioengineering, Communication, Networking and Broadcast Technologies, Components, Circuits, Devices and Systems, Computing and Processing, Engineered Materials, Dielectrics and Plasmas, Engineering Profession, Fields, Waves and Electromagnetics, General Topics for Engineers, Geoscience, Nuclear Engineering, Photonics and Electrooptics, Power, Energy and Industry Applications, Robotics and Control Systems, Signal Processing and Analysis, Transportation
Yıl :
2020
Asif Hassan Syed
Yayıncı Web Sitesi
Tam Metin
Kaydet Alıntıla
The fact that ensemble methods enhance the prediction performance. Therefore, we focused on developing a weighted ensemble method using a novel combination of Cerebrospinal Fluid (CSF) protein biomarkers to predict AD's earlier stages with greater accuracy than the state-of-the-art CSF protein biomarkers. In this regard, two feature selection methods, namely the Recursive Feature Elimination (RFE) and L1 regularization method were used to screen the most important subset of features for building a classification model using the Mild Cognitive Impairment (MCI) dataset. A novel combination of three biomarkers, namely Cystatin C, Matrix metalloproteinases (MMP10), and tau protein, was screened using the linear Support Vector Machine (SVM) and Logistic Regression (LR) classifier based RFE method. Two-tailed unpaired t-test analysis at a 5% significance level showed a significant difference between the mean levels of Cystatin C, MMP10, and tau protein between cognitive normal and cognitively impaired groups. An ensemble model using a weighted average of two best performing classifiers (LR and Linear SVM) was created using a novel subset of three most informative features. Our ensemble model's weighted average results performed significantly better than LR and Linear SVM base classifiers' performance. The Receiver Operating Characteristic Curve (ROC_AUC) and Area under Precision-Recall values (AUPR) of our proposed model were observed to be 0.9799 ± 0.055 0.9108 ± 0.015, respectively. The performance of our proposed weighted averaged ensemble model built using a novel combination of CSF protein biomarkers was significantly better (p <; 0.001) than models generated using different combinations of CSF protein biomarkers obtained from recent studies. An ensemble-learning based application was implemented and deployed at Heroku at https://appsalzheimer.herokuapp.com.
DOI : 10.1109/access.2020.3043715
Anahtar Kelimeler : Biomarkers, Proteins, Biological system modeling, Predictive models, Alzheimer's disease, Feature extraction, Correlation, cognition, diseases, feature selection, learning (artificial intelligence), medical information systems, pattern classification, proteins, regression analysis, support vector machines, recursive feature elimination, L1 regularization method, classification model, Cystatin C, two-tailed unpaired t-test analysis, cognitive normal groups, cognitively impaired groups, weighted average ensemble model, ensemble-learning based application, Alzheimer's disease, cerebrospinal fluid protein biomarkers, CSF protein biomarkers, feature selection method, mild cognitive impairment dataset, Matrix metalloproteinases, linear support vector machine, logistic regression classifier based RFE method, receiver operating characteristic curve, area under precision-recall values, linear SVM base classifiers, Mild cognitive impairment (MCI), cerebrospinal fluid (CSF) protein biomarkers, classification model, online prediction system
ISSN: 2169-3536
Cilt: 8
Sayfa: 222126-222143

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