# Predicting the Failure of Dental Implants Using Supervised Learning Techniques
## 2 Materials and Methods
### Statistical Analysis Tools  
The chi-square test was employed for independent categorical variables, whereas the Mann-Whitney U test or independent samples \(t\)-test were adopted for continuous variables for significant analysis. The data processing was analyzed and presented using SPSS (Version 20.0, IBM Corp, Armonk, NY, USA, 2011). The data mining software WEKA (Version 3.8.1, Machine Learning Group at the University of Waikato, Hamilton, Waikato, New Zealand, 2017) was used to construct the prediction models for three \begin{table}
\begin{tabular}{l l l} \hline
**Group** & **Independent Variables** & **Definition and Code** \\ \hline \multirow{8}{*}{Lifestyle} & Tobacco smoking & 0: Never \\  & Tobacco smoking & 1: Smoking \\  & & 2: Stopped smoking \\ \cline{2-3}  & & 0: Never \\  & & 1: Chewing betel nut \\  & & 2: Stopped chewing betel nut \\ \cline{2-3}  & & 0: Never \\  & Alcohol consumption & 1: Drinking \\  & & 2: Stopped drinking \\ \hline \multirow{4}{*}{Surgeon Background} & \multirow{4}{*}{Departments} & 0: General practice \\  & & 1: Periodontics \\  & & 2: Oral-Maxillary surgery \\ \cline{2-3}  & & Surgeon experience & Ratio scale \\ \hline \multirow{4}{*}{Anatomic Condition} & \multirow{4}{*}{Location of implant} & 0: Maximly anterior teeth \\  & & 1: Maximly pre molars \\  & & 2: Maximly molars \\  & & 3: Mandibular anterior teeth \\  & & 4: Mandibular premolars \\  & & 5: Mandibular molars \\ \cline{2-3}  & & 1: Type I \\  & Bone density & 2: Type II \\  & & 3: Type III \\  & & 4: Type IV \\ \hline \multirow{4}{*}{Surgical Information} & \multirow{4}{*}{Ridge augmentation} & 1: Immediate implant placement \\  & & 2: Early implant placement \\  & & 3: Staged implant placement \\ \cline{2-3}  & & 0: None \\  & & 1:Guided bone regeneration \\  & & 2: Ridge splitting \\ \cline{2-3}  & & Maximly sinus augmentation & 0: None \\  & & 1: Lateral window technique \\  & & 2: Osteotome technique \\ \hline \multirow{4}{*}{Implant attributes} & \multirow{4}{*}{Implant system} & 0: Straumann® \\  & & 1: Ankylos® \\  & & 2: XIVE® \\  & & 3: Nobeactive® \\  & & 4: Branemark® \\  & & 5: Lifecore® \\ \cline{2-3}  & & \multicolumn{1}{c}{Fxture length} & Ratio scale \\ \cline{2-3}  & & \multicolumn{1}{c}{Fxture width} & Ratio scale \\ \hline \multirow{4}{*}{Prosthetics attributed} & \multirow{4}{*}{Types of prosthesis} & 0: Fixed denture \\  & & 1: Overdenture \\ \cline{2-3}  & & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{3-3}  & & 0: Without angle \\ \cline{1-1} \cline{3-3}  & & 1: With angle \\ \cline{1-1} \cline{3-3}  & & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{3-3}  & & 0: Cement-retained \\ \cline{1-1} \cline{3-3}  & & 1: Screw-reained \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._single classifiers: C4.5 decision tree (DT), logistic regression (LGR), and support vector machine (SVM). In addition, two classifier ensembles techniques (i.e., Bagging and AdaBoost) were also considered to improve the prediction performance of the three single classifiers [17; 18; 19]. To avoid the class imbalance problem, we performed ClassBalancer module in WEKA 3.8.1 to reweight the samples so that each class had the same total weight during the phase of model training [20]. Ten-fold cross-validation was then adopted to verify the predictive mode performance, followed by the evaluation of the performance of various prediction models using a confusion matrix (Table 2). In Table 2, TP represents the number of failure implant surgeries that were predicted to fail, TN represents the number of success surgeries that were predicted to succeed, FP represents the number of successful surgeries that were predicted to fail, and FN represents the number of failure surgeries that were predicted to be succeed. Finally, the forecast capacities of each prediction model were assessed through sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC).