# Predicting the Failure of Dental Implants Using Supervised Learning Techniques
###### Abstract  
Prosthodonic 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 prosthodotics treatments for their patients. artificial dental implant surgery; data mining; dental implant failure; supervised learning techniques +
Footnote †: journal: Applied  
for occlusion and aesthetic functions. The treatment can be divided into two stages: in the first stage, the fixture is surgically implanted into the alveolar bone; after osseointegration is completed three to six months later, the second stage (prosthodonic treatment) is executed. The treatment is completed after the abutment and prosthesis are connected to the fixture. In most studies, the success criteria for dental implants are based on the standards proposed by [PERSON] et al. (1986) [PERSON] et al. (1986). * The X-ray film shows no signs of radiolucency in the alveolar bone around the fixture. * Alveolar bone resorption around the fixture does not exceed 1 mm within the first year after the dental implant. * The annual bone loss around the fixture does not exceed 0.2 mm. According to the fixture quality stipulated during the 2007 consensus conference by the International Congress of Oral Implantologists, dental implant failure is defined as follows [3]. * Pain and shakiness during occlusion. * Bone loss does not exceed half the fixture length under X-ray. * Uncontrollable exudation around the implant. * The implant is no longer in the mouth. Previous studies have revealed many factors affecting the failure of dental implants, including individual patient factors, the location of the implant, bone density, fixture, prosthodonics factors, the implant surgery method, implant timing, and the skew angle of the abutment [4; 5; 6; 7]. In addition, because implant treatments involve delicate surgical stages, the clinical surgeons' training backgrounds and their surgical operation experience are key factors directly affecting successful treatments; however, currently only two studies have mentioned their relationships to implant treatment results [8; 9]. Furthermore, the two main abutment and crown fixation methods are cementing and screw-retention. Most clinical physicians select the appropriate approach according to their clinical experience and the conditions of each specific case.