Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance

Tian, Xiaolu; Chong, Yutian; Huang, Yutao; Guo, Pi; Li, Mengjie; Zhang, Wangjian; Du, Zhicheng; Li, Xiangyong; Hao, Yuantao

doi:10.1155/2019/6915850

Cited by 50 publications

(28 citation statements)

References 22 publications

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“…The predictive power of the classi ers was investigated by the area under the ROC curve (AUC), in women's suicidal ideation, the area under ROC curve of the DT in the training sample is higher than LR and RF, in a similar study, the area under ROC curve for the DT is better than LR and discriminant analysis (42). But in the validation sample, the area under the ROC curve of RF is higher than the LR and DT, in Tian study, the area under the ROC curve is better for RF than DT and LR (43). The sensitivity range between 0.2 (the validation sample of the RF for women's suicidal ideation) was 0.85 (the training sample of the LR for women's suicidal ideation).…”

Section: Discussionmentioning

confidence: 60%

A Comparison of Random Forest and Decision Tree for Suicide Ideation Classification

Mohammadian-Khoshnoud

Omidi

Faradmal

et al. 2020

Preprint

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Background: Suicide resulted from complex interaction factors. Most classical statistical methods were not efficiently enough to cover this complexity. With the new branch of statistics as statistical/machine learning, complex relationships between risk factors and responses can be modeled. Methods: We aimed to identify the high-risk groups for suicide using different classification methods including logistic regression(LR), decision tree(DT), and random forest(RF). Also, the prediction accuracy of the models is compared. This study used data obtained from a cross-sectional study conducted in in the Hamadan University of Medical Sciences from 2015-2016 to investigate the prevalence of suicidal ideation and related risk factors among university students. The LR, DT, and RF models were used to evaluate the high-risk group for suicide. Finally, the applied all three models were compared using sensitivity(SE), specificity(SP), and the area under receiver operating characteristics (ROC) curves. Results: In the training sample, the area under the ROC curve of the DT was greater than the LR and RF. But in the validation sample, the RF model has the best performance and the DT has the worst performance among these methods.Discussion: In this study, the risk factors for suicide were different for men and women. According to the results of the DT, substance abuse, average, general health score, faculty of education, depression were the risk factors on suicidal ideation in both genders. But despair about the future, residence (parents' house/dormitory) were among the factors contributing to the suicidal ideation of men. On the other hand, parents’ education, interested in the discipline and anxiety influence factors on suicidal ideation in women. The results of RF indicated that depression, general health score, average, anxiety and substance abuse were important risk factors for suicidal ideation in both genders. Also, the faculty of education and age are risk factors for suicide in women. Conclusions: In the training sample, the DT had better performance but in the validation sample, the RF model provided better results. The LR was the best model for diagnosis of the patient and the DT and RF are considered the best models to diagnosis a healthy individual.

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Section: Discussionmentioning

confidence: 60%

A Comparison of Random Forest and Decision Tree for Suicide Ideation Classification

Mohammadian-Khoshnoud

Omidi

Faradmal

et al. 2020

Preprint

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“…It can detect complex underlying patterns of features to predict the binary target variable of belonging to the ASD group. This algorithm gives state-of-the-art results in a wide range of classification applications, especially in healthcare and diagnosis of diseases [45,46,72,73].…”

Section: Classification Processmentioning

confidence: 99%

Pregnancy data enable identification of relevant biomarkers and a partial prognosis of autism at birth

Caly

Rabiei

Coste-Mazeau

et al. 2020

Preprint

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AbstractAttempts to extract early biomarkers and expedite detection of Autism Spectrum Disorder (ASD) have been centered on postnatal measures of babies at familial risk. Here, we suggest that it might be possible to do these tasks already at birth relying on ultrasound and biological measurements routinely collected from pregnant mothers and fetuses during gestation and birth. We performed a gradient boosting decision tree classification analysis in parallel with statistical tests on a population of babies with typical development or later diagnosed with ASD. By focusing on minimization of the false positive rate, the cross-validated specificity of the classifier reached to 96% with a sensitivity of 41% and a positive predictive value of 77%. Extracted biomarkers included sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femoral length in the 3rd trimester, white cells in the 3rd trimester, fetal heart rate during labour, newborn feeding and newborn’s temperature difference between birth and one day after. Statistical models revealed that 38% of babies later diagnosed with ASD had significantly larger fetal cephalic perimeter than age matched neurotypical babies, suggesting an in-utero origin of the bigger brains of toddlers with ASD. Results pave the way to use pregnancy follow-up measurements to provide an early prognosis of ASD and implement pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.

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“…Runmin et al constructed and compared machine learning algorithms with the FIB-4 score for the clinical prediction of HBV [13]. Xiaolu et al applied different machine learning algorithms on the clinical data for predicting HBsAg seroclearance [14]. These methods and techniques are limited to the development of an algorithm only.…”

Section: Introductionmentioning

confidence: 99%

Rapid Detection System for Hepatitis B Surface Antigen (HBsAg) Based on Immunomagnetic Separation, Multi-Angle Dynamic Light Scattering and Support Vector Machine

Hussain¹,

Zhu²,

Yang³

et al. 2020

IEEE Access

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Hepatitis B virus (HBV) is a significant public health problem worldwide. Hepatitis B surface antigen (HBsAg) is the principle marker for laboratory testing of HBV, but the rapid identification of HBsAg is challenging in a resource-limited setting. Antibodies to HBsAg (Anti-HBs) levels are measured as markers for an immune response to vaccination as well as for decision making for specific treatment against Hepatitis-B. This research developed a prototype for the rapid detection of HBsAg using immunomagnetic separation, dynamic light scattering, and support vector machine. Magnetic beads coated with polyclonal anti-HBsAg were used to isolate HBsAg from the sample. The performance characteristics of quantitative real-time detection of HBsAg were characterized under optimized conditions. Twelve photodetectors were arranged on four concentric curvatures at different angles. The photodetectors were positioned around the sample flask in forward direction. The prototype acquires the real-time laser scattering light from the sample, and the noise was removed. The power spectral features were extracted from the acquired signal. Support vector machines (SVM) were used for training a classification algorithm by using extracted features. The overall classification accuracy for the identification of HBsAg was 87.7%. The HBsAg detection test was also performed on 20 serum specimens, with 10 serum samples were positive for HBsAg and 10 were healthy control subjects. The test had a dynamic range of 98.86 IU/mL to 3163.5 IU/mL. Results of HBsAg detection agreed completely with those of conventional Chemiluminescence Immunoassay (CLIA). In conclusion, the proposed HBsAg detection method can differentiate the sample that contains HBsAg enriched IM beads and blank IM beads.

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Using Machine Learning Algorithms to Predict Hepatitis B Surface Antigen Seroclearance

Cited by 50 publications

References 22 publications

A Comparison of Random Forest and Decision Tree for Suicide Ideation Classification

A Comparison of Random Forest and Decision Tree for Suicide Ideation Classification

Pregnancy data enable identification of relevant biomarkers and a partial prognosis of autism at birth

Rapid Detection System for Hepatitis B Surface Antigen (HBsAg) Based on Immunomagnetic Separation, Multi-Angle Dynamic Light Scattering and Support Vector Machine

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