Resolution invariant wavelet features of melanoma studied by SVM classifiers

Surówka, G.; Ogorzalek, M.J.

doi:10.1371/journal.pone.0211318

Cited by 4 publications

(1 citation statement)

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“…SVM is available for both classification and regression analysis, and is a supervised learning model for pattern recognition and data analysis in ML. SVM is a method of computing hyperplanes that optimally separate data belonging to two classes [ 39 ]. In addition to linear classification, SVM also enables nonlinear classification using kernel tricks.…”

Section: Methodsmentioning

confidence: 99%

Machine learning prediction of dropping out of outpatients with alcohol use disorders

Lee

Kim

et al. 2021

PLoS ONE

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Background Alcohol use disorder (AUD) is a chronic disease with a higher recurrence rate than that of other mental illnesses. Moreover, it requires continuous outpatient treatment for the patient to maintain abstinence. However, with a low probability of these patients to continue outpatient treatment, predicting and managing patients who might discontinue treatment becomes necessary. Accordingly, we developed a machine learning (ML) algorithm to predict which the risk of patients dropping out of outpatient treatment schemes. Methods A total of 839 patients were selected out of 2,206 patients admitted for AUD in three hospitals under the Catholic Central Medical Center in Korea. We implemented six ML models—logistic regression, support vector machine, k-nearest neighbor, random forest, neural network, and AdaBoost—and compared the prediction performances thereof. Results Among the six models, AdaBoost was selected as the final model for recommended use owing to its area under the receiver operating characteristic curve (AUROC) of 0.72. The four variables affecting the prediction based on feature importance were the length of hospitalization, age, residential area, and diabetes. Conclusion An ML algorithm was developed herein to predict the risk of patients with AUD in Korea discontinuing outpatient treatment. By testing and validating various machine learning models, we determined the best performing model, AdaBoost, as the final model for recommended use. Using this model, clinicians can manage patients with high risks of discontinuing treatment and establish patient-specific treatment strategies. Therefore, our model can potentially enable patients with AUD to successfully complete their treatments by identifying them before they can drop out.

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Section: Methodsmentioning

confidence: 99%