Assessment of the risk factors for vitamin D3 deﬁciency in chronic hepatitis B patient using the decision tree learning algorithm in Birjand

Osmani, Freshteh; Ziaee, Masood

doi:10.1016/j.imu.2021.100519

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Various studies have been conducted to assess the knowledge of dental health care providers regardingCOVID-19 recently 10,11 . Therefore, the knowledge of infectious diseases of dental patients is very important.…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Related Knowledge among Dental Patients- A Questionnaire Survey

EBRAHİMİANBAGHAN¹,

OSMANİ

2022

Cumhuriyet Dental Journal

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Section: Discussionmentioning

confidence: 99%

COVID-19 Related Knowledge among Dental Patients- A Questionnaire Survey

EBRAHİMİANBAGHAN¹,

OSMANİ

2022

Cumhuriyet Dental Journal

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“…For our VDD severity modeling, each node in the tree predicts the deficiency severity and each branch indicates the states of the variable [19].…”

Section: Methodsmentioning

confidence: 99%

A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model

Osmani¹

2023

Russian Journal of Infection and Immunity

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Background: VD is involved in various pathophysiological mechanisms in a plethora of diseases. And also, there is a strong demand for the prediction of its severity using different methods. The study aims to evaluate performance of DT as one of the machine learning models in the prediction of severity in VDD. Methods: In total, data containing serum Vitamin D levels were collected from 292 CHB patients. The independent characteristics such as: age, sex, weight, height, zinc, BMI, body fat, sunlight exposure, and milk consumption were used for prediction of VDD. 60% of them were allocated to a training dataset randomly. To evaluate the performance of decision-tree the remaining 40% were used as the testing dataset. The validation of the model was evaluated by ROC curve.Results: The prevalence of vitaminD3 deficiency was high among patients (63.0%). The final experimentation results showed that DT Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. Conclusion: We concluded that the serum level of Zn is an important associated risk factor for identifying cases with vitamin D deficiency. Also, the risk of VDD could be predicted with high accuracy using decision tree learning algorithm that could be used for antiviral therapy in CHB patients.

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“…The process repeats until the value of Gini impurity gets as close to or equal to 0 as possible, and the data subsets associated with the internal nodes get smaller as the tree grows deeper with increasing steps. Finally, the tree reaches its leaf nodes or end nodes, representing the final decisions made by each branch and all its corresponding internal nodes (Osmani and Ziaee, 2021).…”

Section: Methodsmentioning

confidence: 99%

Predictors of underutilization of lung cancer screening: a machine learning approach

Guo¹,

Yin²,

Chen

et al. 2022

European Journal of Cancer Prevention

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Lung cancer is the second common cancer and a leading cause of cancer-related death in the US. Unfavorably, the prevalence of using low-dose computed tomography (LDCT) for lung cancer prevention in the US has remained below 4% over time. The purpose of this study is to develop machine learning models to analyze interactive pathways of factors associated with lung cancer screening use with the LDCT. The study was based on the data retrieved from the 2018 Behavioral Risk Factor Surveillance System. After dealing with missing values, 86 variables and 710 samples were included in the decision tree model and the random forest model. The data were randomly split into training (569/710, 80%) and testing (141/710, 20%) sets. Gini impurity is used to select and determine the optimal split of the nodes in the model. Machine learning performance was evaluated by model accuracy, sensitivity, specificity, F1 score, etc. The average performance metrics of the decision tree model were obtained: average accuracy is 67.78%, F1 score is 65.76%, sensitivity is 62.52%, and specificity is 73.57% based on 100 runs. In the decision model, nine interactive pathways were identified among the following factors: average drinks per month, BMI, diabetes, first smoke age, years of smoking, year(s) quit smoking, sex, last sigmoidoscopy or colonoscopy, last dental visit, general health, insurance, education, and last Pap test. Lung cancer screening utilization is the result of the interplay of multifactors. Lung cancer screening programs in clinical settings should not only focus on patients’ smoking behaviors but also consider other socioeconomic factors.

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Assessment of the risk factors for vitamin D3 deﬁciency in chronic hepatitis B patient using the decision tree learning algorithm in Birjand

Cited by 6 publications

References 25 publications

COVID-19 Related Knowledge among Dental Patients- A Questionnaire Survey

COVID-19 Related Knowledge among Dental Patients- A Questionnaire Survey

A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model

Predictors of underutilization of lung cancer screening: a machine learning approach

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