A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery

Sheikhtaheri, Abbas; Orooji, Azam; Pazouki, Abdolreza; Beitollahi, Maryam

doi:10.1007/s11695-019-03849-w

Cited by 19 publications

(16 citation statements)

References 40 publications

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“…Machine learning algorithms, such as DTs (decision trees) [11], SVMs (support vector machines) [12]- [14] and ANNs (artificial neural networks) [15], combined to the powerful deep learning approach [16], can be used to tackle various challenging issues arising in bioinformatics and healthcare science, including protein structure prediction and disease identification [17], [18]. Nowadays, one of the most relevant challenges in healthcare science is the improvement of the performance of Clinical Decision Support Systems (CDSS) [19] meant to predict and cure many important diseases such as Coronary Artery Disease (CAD) and other cardiovascular diseases [20], obesity [21], Chronic Obstructive Pulmonary Disease (COPD) [22], Alzheimer disease [23], prostate cancer [24], etc. CDSSs can be very beneficial to diagnose many of these diseases, including CAD, which is one of the major types of heart diseases [20] According to recent reports, CAD is the most common cardiovascular disease in the United States of America, being the leading cause of heart attacks among both male and female population [25].…”

Section: Introductionmentioning

confidence: 99%

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

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Coronary artery disease (CAD) is one of the main causes of cardiac death around the world. Due to its significant impact on the society, early and accurate detection of CAD is essential. This study proposes a novel nested ensemble nu-Support Vector Classification (NE-nu-SVC) model which combines several traditional machine learning methods and ensemble learning techniques for effective diagnosis of CAD. We validated our model using two well-known CAD datasets (Z-Alizadeh Sani and Cleveland). To improve the performance of the model, we selected clinically significant features from the datasets using a genetic search algorithm. To further improve our results, we applied a multi-level filtering technique to balance the data using the ClassBlancer and Resample methods. Our base algorithm, nu-SVC, is performed using four well-known kernel functions (linear, polynomial, radial basis (RBF) and sigmoid). The proposed NE-nu-SVC model provided the highest accuracy of 94.66% and 98.60% to predict CAD entities in the Z-Alizadeh Sani and Cleveland CAD datasets, respectively. Our system can aid the clinicians to diagnose CAD accurately and may probably replace other invasive diagnostic techniques. INDEX TERMS Coronary artery disease (CAD), machine learning, ensemble learning, nested ensemble (NE) model, genetic algorithm.

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Section: Introductionmentioning

confidence: 99%

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

et al. 2019

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“…Different methods such as imputation with mean, median, or mode are common. We imputed the missing values with the mean for age and the highest frequency of values for nonnumerical variables as well [11,33].…”

Section: Imputing Missing Variablesmentioning

confidence: 99%

“…ere are various methods such as oversampling the minor class or undersampling the major class to solve this problem [11,12]. We oversampled the death cases to create more balanced datasets.…”

Section: Data Balancingmentioning

confidence: 99%

“…We randomly divided the data into two sets, training (70%) and testing (30%) sets, and developed our models using common machine learning algorithms that are usually reported to perform well in medicine including Multiple Layer Perceptron (MLP) neural networks [11,12,34], Chi-Squared Detection of Automatic Interaction (CHAID), C5, and Random Forest (RF) decision trees [11,12,33,34], Support Vector Machine (SVM) with Radial Basic Function (RBF) kernel [12,35,36], and Bayesian network [12,[37][38][39].…”

Section: Model Development and Evaluationmentioning

confidence: 99%

“…e use of modern and noninvasive methods to triage patients into specific and known categories at the early stages of the disease is beneficial [10]. One of these approaches is the use of predictive models based on machine learning [11,12]. For example, developing predictive models based on mortality risk factors can positively prevent mortality through controlling acute conditions and planning in intensive care units [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations

Zarei

Jamshidnezhad

Shoushtari

et al. 2022

Journal of Healthcare Engineering

Self Cite

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Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

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Biomarkers and Machine Learning Applications in Obesity

Tsave

Kavakiotis

2020

Obesity and Diabetes

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A Clinical Decision Support System for Predicting the Early Complications of One-Anastomosis Gastric Bypass Surgery

Cited by 19 publications

References 40 publications

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

NE-nu-SVC: A New Nested Ensemble Clinical Decision Support System for Effective Diagnosis of Coronary Artery Disease

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations

Biomarkers and Machine Learning Applications in Obesity

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