Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis

Cooray, Upul; Watt, Richard G.; Tsakos, Georgios; Heilmann, Anja; Hariyama, Masanori; Yamamoto, Takafumi; Kuruppuarachchige, Isuruni; Kondo, Katsunori; Osaka, Ken; Aida, Jun

doi:10.1016/j.socscimed.2021.114486

Cited by 26 publications

(27 citation statements)

References 31 publications

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“…Both the excluded studies made predictions without boosting the algorithm. Finally, five studies formed the basis of this study [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

“…A summary of the general information of each study is presented in Table 1 . Four of the five included studies were development/validation studies [ 17 , 18 , 19 , 20 ]. Another study was a development study [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

“…The number of patients included in each study ranged from 94 to 11,977. Of the four validation studies, two studies used “Hold-out validation” [ 17 , 18 ], and one study used “10-fold cross validation” [ 19 ]. All studies employed oral variables as predictors.…”

Section: Resultsmentioning

confidence: 99%

“…All studies employed oral variables as predictors. Furthermore, two studies used socioeconomic variables [ 19 , 20 ]. Three out of the five studies included predictions made at the tooth level [ 17 , 18 , 21 ], and the two other studies with socioeconomic variables made predictions at the patient level [ 19 , 20 ].…”

Section: Resultsmentioning

confidence: 99%

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Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Hasuike

Watanabe

Wakuda

et al. 2022

JPM

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Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.

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“…Both the excluded studies made predictions without boosting the algorithm. Finally, five studies formed the basis of this study [ 17 , 18 , 19 , 20 , 21 ].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Hasuike

Watanabe

Wakuda

et al. 2022

JPM

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“…Reports from Europe and the United States have disclosed that machine learning analysis could detect associations between factors that differ from traditional logistic regression models and may have a higher ability to predict mortality 3 , 5 . In Japan, machine learning-based models have been used in a few studies to predict disease incidences such as COPD 6 and tooth loss 7 ; however, there are no reports on the prediction of mortality. It will be helpful to identify high-risk groups for mortality and provide lifestyle guidance.…”

Section: Introductionmentioning

confidence: 99%

Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study

Kawano

Otaki

Suzuki

et al. 2022

Sci Rep

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Early detection and treatment of diseases through health checkups are effective in improving life expectancy. In this study, we compared the predictive ability for 5-year mortality between two machine learning-based models (gradient boosting decision tree [XGBoost] and neural network) and a conventional logistic regression model in 116,749 health checkup participants. We built prediction models using a training dataset consisting of 85,361 participants in 2008 and evaluated the models using a test dataset consisting of 31,388 participants from 2009 to 2014. The predictive ability was evaluated by the values of the area under the receiver operating characteristic curve (AUC) in the test dataset. The AUC values were 0.811 for XGBoost, 0.774 for neural network, and 0.772 for logistic regression models, indicating that the predictive ability of XGBoost was the highest. The importance rating of each explanatory variable was evaluated using the SHapley Additive exPlanations (SHAP) values, which were similar among these models. This study showed that the machine learning-based model has a higher predictive ability than the conventional logistic regression model and may be useful for risk assessment and health guidance for health checkup participants.

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Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics

Lee

Zhang

et al. 2023

Journal of Periodontology

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BackgroundThis study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach.MethodsInformation on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two‐step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root‐mean‐squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model.ResultsIn total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models.ConclusionThe two‐step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule‐based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

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Importance of socioeconomic factors in predicting tooth loss among older adults in Japan: Evidence from a machine learning analysis

Cited by 26 publications

References 31 publications

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment

Prediction of mortality risk of health checkup participants using machine learning-based models: the J-SHC study

Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics

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