Support vector machine-based prediction of pore-forming toxins (PFT) using distributed representation of reduced alphabets

Bhosale, Hrushikesh; Ramakrishnan, Vigneshwar; Jayaraman, Vaidyanathan

doi:10.1142/s0219720021500281

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…A decision tree is a tree-shaped structure in which each internal node represents a judgment on an attribute, each branch represents the output of a judgment result, and finally each leaf node represents a classification result. SVM ( 25 ) is a fast and dependable classification algorithm that performs very well with a limited amount of data. For classification, SVM works by creating a decision boundary in between our data points, that tries to separate it as best as possible.…”

Section: Methodsmentioning

confidence: 99%

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

Cai

Xiao

Zou

et al. 2022

Front. Cardiovasc. Med.

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BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.

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Section: Methodsmentioning

confidence: 99%

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

Cai

Xiao

Zou

et al. 2022

Front. Cardiovasc. Med.

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“…Based on supervised learning algorithms, namely random forest classifier(RFC), artificial neural network(ANN), support vector machine(SVM), decision tree(DT), and extreme gradient boosting gradient-(XGboost) algorithm, the CLNM prediction model was constructed. [20][21][22][23][24][25] Additionally, the prediction efficiency of the MLbased model was evaluated via receiver operating characteristic curve(ROC) and decision curve analysis(DCA).…”

Section: Construction Of Ml-based Prediction Modelmentioning

confidence: 99%

Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models

Xue¹,

Zhang²,

He³

et al. 2022

CMAR

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Purpose Although many factors determine the prognosis of papillary thyroid carcinoma (PTC), cervical lymph node metastasis (CLNM) is one of the most terrible factors. In view of this, this study aimed to build a CLNM prediction model for papillary thyroid microcarcinoma (PTMC) with the help of machine learning algorithm. Methods We retrospectively analyzed 387 PTMC patients hospitalized in the Department of Medical Oncology, Enshi Tujia and Miao Autonomous Prefecture Central Hospital from January 1, 2015, to January 31, 2022. Based on supervised learning algorithms, namely random forest classifier (RFC), artificial neural network(ANN), support vector machine(SVM), decision tree(DT), and extreme gradient boosting gradient(XGboost) algorithm, the LNM prediction model was constructed, and the prediction efficiency of ML-based model was evaluated via receiver operating characteristic curve(ROC) and decision curve analysis(DCA). Results Finally, a total of 24 baseline variables were included in the supervised learning algorithm. According to the iterative analysis results, the pulsatility index(PI), resistance index(RI), peak systolic blood flow velocity(PSBV), systolic acceleration time(SAT), and shear wave elastography elastic index(SWEEI), such as average value(Emean), maximum value(Emax), and minimum value(Emix) were candidate predictors. Among the five supervised learning models, RFC had the strongest prediction efficiency with area under curve(AUC) of 0.889 (95% CI: 0.838–0.940) and 0.878 (95% CI: 0.821–0.935) in the training set and testing set, respectively. While ANN, DT, SVM and XGboost had prediction efficiency between 0.767 (95% CI: 0.716–0.818) and 0.854 (95% CI: 0.803–0.905) in the training set, and ranged from 0.762 (95% CI: 0.705–0.819) to 0.861 (95% CI: 0.804–0.918) in the testing set. Conclusion We have successfully constructed an ML-based prediction model, which can accurately classify the LNM risk of patients with PTMC. In particular, the RFC model can help tailor clinical decisions of treatment and surveillance.

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Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction

Wei,

Cai,

Xiao

et al. 2024

Heliyon

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Support vector machine-based prediction of pore-forming toxins (PFT) using distributed representation of reduced alphabets

Cited by 5 publications

References 51 publications

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

Predicting acute kidney injury risk in acute myocardial infarction patients: An artificial intelligence model using medical information mart for intensive care databases

Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models

Artificial intelligence algorithms permits rapid acute kidney injury risk classification of patients with acute myocardial infarction

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