Feature-ranking-based ensemble classifiers for survivability prediction of intensive care unit patients using lab test data

Alam, Zahangir; Masud, Mohammad Mehedy; Rahman, Mohammad Saifur; Cheratta, Muhsin; Nayeem, Muhammad Ali; Rahman, Mahfuzur

doi:10.1016/j.imu.2020.100495

Cited by 4 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This provided a modest improvement in outcome prediction as well. Another study, by Alam et al [21], involved the use of feature-ranking-based ensemble classifiers to predict survivability among ICU patients. The implications of feature ranking can improve model performance in all datasets as well as all algorithms.…”

Section: Discussionmentioning

confidence: 99%

Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome

Limprasert

Phu-ang

2023

Healthc Inform Res

View full text Add to dashboard Cite

Objectives: This study compared feature selection by machine learning or expert recommendation in the performance of classification models for in-hospital mortality among patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI).Methods: A dataset of 1,123 patients with ACS who underwent PCI was analyzed. After assigning 80% of instances to the training set through random splitting, we performed feature scaling and resampling with the synthetic minority over-sampling technique and Tomek link method. We compared two feature selection methods: recursive feature elimination with cross-validation (RFECV) and selection by interventional cardiologists. We used five simple models: support vector machine (SVM), random forest, decision tree, logistic regression, and artificial neural network. The performance metrics were accuracy, recall, and the false-negative rate, measured with 10-fold cross-validation in the training set and validated in the test set.Results: Patients’ mean age was 66.22 ± 12.88 years, and 33.63% had ST-elevation ACS. Fifteen of 34 features were selected as important with the RFECV method, while the experts chose 11 features. All models with feature selection by RFECV had higher accuracy than the models with expert-chosen features. In the training set, the random forest model had the highest accuracy (0.96 ± 0.01) and recall (0.97 ± 0.02). After validation in the test set, the SVM model displayed the highest accuracy (0.81) and a recall of 0.61.Conclusions: Models with feature selection by RFECV had higher accuracy than those with feature selection by experts in identifying patients with ACS at high risk for in-hospital mortality.

show abstract

Section: Discussionmentioning

confidence: 99%

Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome

Limprasert

Phu-ang

2023

Healthc Inform Res

View full text Add to dashboard Cite

show abstract

“…Three machine learning algorithms following other studies ( 19 , 26 , 27 ) available in this contextual domain and/or in other similar domains, i.e., Random Forest (RF), Support Vector Machine (SVM) (using Radial Basis Function kernel), and Linear Regression/Logistic Regression (for the binary task), were implemented to develop the predictive models. Training the models was undertaken on the “training set,” and 10-fold cross-validation was used to measure the models training performances.…”

Section: Methodsmentioning

confidence: 99%

Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach

Alam

Brillantino

et al. 2022

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

IntroductionTo self-monitor asthma symptoms, existing methods (e.g. peak flow metre, smart spirometer) require special equipment and are not always used by the patients. Voice recording has the potential to generate surrogate measures of lung function and this study aims to apply machine learning approaches to predict lung function and severity of abnormal lung function from recorded voice for asthma patients.MethodsA threshold-based mechanism was designed to separate speech and breathing from 323 recordings. Features extracted from these were combined with biological factors to predict lung function. Three predictive models were developed using Random Forest (RF), Support Vector Machine (SVM), and linear regression algorithms: (a) regression models to predict lung function, (b) multi-class classification models to predict severity of lung function abnormality, and (c) binary classification models to predict lung function abnormality. Training and test samples were separated (70%:30%, using balanced portioning), features were normalised, 10-fold cross-validation was used and model performances were evaluated on the test samples.ResultsThe RF-based regression model performed better with the lowest root mean square error of 10·86. To predict severity of lung function impairment, the SVM-based model performed best in multi-class classification (accuracy = 73.20%), whereas the RF-based model performed best in binary classification models for predicting abnormal lung function (accuracy = 85%).ConclusionOur machine learning approaches can predict lung function, from recorded voice files, better than published approaches. This technique could be used to develop future telehealth solutions including smartphone-based applications which have potential to aid decision making and self-monitoring in asthma.

show abstract

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

Ben‐Assuli

Heart

Klempfner

et al. 2023

Decision Support Systems

View full text Add to dashboard Cite

Feature-ranking-based ensemble classifiers for survivability prediction of intensive care unit patients using lab test data

Cited by 4 publications

References 23 publications

Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome

Data Modeling Using Vital Sign Dynamics for In-hospital Mortality Classification in Patients with Acute Coronary Syndrome

Predicting Pulmonary Function From the Analysis of Voice: A Machine Learning Approach

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

Contact Info

Product

Resources

About