A retrospective analysis of prognostic indicators in dental implant therapy using the C5.0 decision tree algorithm

Chiang, Hsi-Jen; Tseng, Chun-Chieh; Torng, Chau-Chen

doi:10.1016/j.jds.2013.04.009

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…The decision tree is an artificial intelligence method used in machine learning and data mining; its scope is to discover the predictive structure of a problem. 19 In the present study, the decision tree identified valid standards and relationships correlating variables with discomfort for EC prescription.…”

Section: Methodsmentioning

confidence: 89%

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Why are pediatricians uncomfortable with prescribing emergency contraception for adolescents?

Amorim

Barbieri

Bôtto-Menezes

et al. 2023

Rev. paul. pediatr.

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Objective: Emergency contraception (EC) is an effective and safe method for preventing unplanned pregnancy after unprotected sexual intercourse among adolescents but is infrequently prescribed by pediatricians. Because of the scarcity of data on the discomfort with EC prescription among physicians in Brazil, this study aimed to identify associated factors with discomfort with EC prescription among pediatricians in the state of Amazonas. Methods: A web-based, cross-sectional study including sociodemographic data, knowledge, attitudes, and discomfort with EC prescription was used. Multivariate logistic regression and artificial intelligence methods such as decision tree and random forest analysis were used to identify factors associated with discomfort with EC prescriptions. Results: Among 151 physicians who responded to the survey, 53.0% were uncomfortable with prescribing EC, whereas only 33.1% had already prescribed it. Inexperience was significantly associated with discomfort with EC prescription (odds ratio 4.47, 95% confidence interval 1.71–11.66). Previous EC prescription was protective against discomfort with EC prescription in the three models. Conclusions: EC is still infrequently prescribed by pediatricians because of inexperience and misconceptions. Training these professionals needs to be implemented as part of public health policies to reduce unplanned adolescent pregnancy.

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Section: Methodsmentioning

confidence: 89%

“…20 K-fold cross-validation was used to test the models' performances in our study, consisting of two phases, namely, training and validation. 19 Briefly, our database was split into five equal or almost equal subsets (k=5). The first subset was the validation set, and the others were training subsets.…”

Section: Methodsmentioning

confidence: 99%

Why are pediatricians uncomfortable with prescribing emergency contraception for adolescents?

Amorim

Barbieri

Bôtto-Menezes

et al. 2023

Rev. paul. pediatr.

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“…Accurately classifying contraceptives is an essential task for healthcare providers and policymakers. Additionally, contraceptives play a crucial role in family planning, allowing couples to make informed decisions about the timing and spacing of their children Chiang, et al [3].…”

Section: Background To the Studymentioning

confidence: 99%

Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique<br /> <div> <br /> </div>

Kiprop,

Wamwea,

Imboga

et al. 2023

AJTAS

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The Kenyan government in partnership with other stakeholders involved in providing family planning services have put in place various strategies and policies to increase uptake of contraceptives. This results in an increase in contraceptive prevalence rate (CPR), reduction of both total fertility rate (TFR) and sexually transmitted infections (STIs). Despite the various strategies and policies, the total fertility rate still remains high, while CPR has been unattained, respectively. The aim of this study was to classify contraceptives use among undergraduate students using a supervised machine learning technique. The target population constituted students at Jomo Kenyatta University of Agriculture and Technology (JKUAT) (Eldoret Campus). The study applied simple random sampling technique to obtain data from a sample of 252 using structured questionnaires. A decision tree classifier based on CHAID and C5.0 algorithms were used for classification. Pearson Chi-Squared statistic was used as feature selection technique to rank significant factors influencing contraceptives use based on their Chi scores. The findings show that the use of Chi-Squared feature selection led to contraceptives factors that were ranked higher having higher classification performance. The fitted decision tree model based on CHAID algorithm had a higher classification accuracy of 64.68% with 195 correct classifications as compared to the C5.0 decision tree model with accuracy of 61.18% with 163 correct classifications. The study findings contribute to a better insight on the classifications of contraceptives use among undergraduate students in Kenya. Hence, the government of Kenya can implement policies to enhance contraceptives awareness.

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“…In addition, the independent t-test in this study verified that fixture width is correlated with late fixture failure. According to Chiang et al (2013), a one-unit increase in fixture length increases the fixture failure rate 2.9 times, indicating that fixture width plays a crucial role once the fixture is connected to the prosthesis and starts withstanding occlusion force [14].…”

Section: • Fixture Widthmentioning

confidence: 99%

“…To analyze implant success and failure evaluations, Chiang et al (2013) conducted a retrospective study using C5.0 DT to analyze the critical factors of implant surgeries. A total of 1161 fixtures from 513 patients at three dental clinics were collected [14]. The results demonstrated a 0.825 sensitivity and a 0.992 specificity; prosthodontists can predict results of dental implant surgery based on a patients' physical status and implant characteristics by the DT classifier.…”

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confidence: 99%

Predicting the Failure of Dental Implants Using Supervised Learning Techniques

Liu

Lin

et al. 2018

Applied Sciences

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Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients' body conditions, surgeons' experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients' systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT), support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost) were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741); the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

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A retrospective analysis of prognostic indicators in dental implant therapy using the C5.0 decision tree algorithm

Cited by 12 publications

References 14 publications

Why are pediatricians uncomfortable with prescribing emergency contraception for adolescents?

Why are pediatricians uncomfortable with prescribing emergency contraception for adolescents?

Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique<br /> <div> <br /> </div>

Predicting the Failure of Dental Implants Using Supervised Learning Techniques

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A retrospective analysis of prognostic indicators in dental implant therapy using the C5.0 decision tree algorithm

Cited by 12 publications

References 14 publications

Why are pediatricians uncomfortable with prescribing emergency contraception for adolescents?

Why are pediatricians uncomfortable with prescribing emergency contraception for adolescents?

Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique&lt;br /&gt; &lt;div&gt; &lt;br /&gt; &lt;/div&gt;

Predicting the Failure of Dental Implants Using Supervised Learning Techniques

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Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique<br /> <div> <br /> </div>