## Scientific Background & Dataset Creation

The dataset for this competition is a **synthetic but realistic** collection of 10,000 patient records, meticulously crafted based on findings from four recent, high-impact studies in dental literature. It includes 20 clinically significant features that have been shown to influence implant outcomes.

### Key Literature Review

Our research involved a review of several recent, high-impact studies that employed both traditional statistical analysis and advanced machine learning models to identify predictors of implant success and failure. The following table summarizes the key articles that form the basis of this dataset.

| Study | Year | Sample Size (Implants) | Follow-up Period | Key Finding |
|-------|------|------------------------|------------------|-------------|
| French et al. [1] | 2021 | 10,871 | Up to 22 years | Smoking and diabetes are significantly correlated with higher implant failure rates. |
| Mahendra et al. [2] | 2025 | 161 | 5+ years | Female gender, non-smoking status, and absence of parafunctional habits were associated with higher survival rates. |
| Shahapur et al. [3] | 2024 | Not specified | Not specified | Bruxism, peri-implantitis, smoking, and diabetes were identified as the most significant predictors of implant failure using a decision tree model. |
| Lyakhov et al. [4] | 2022 | 1,626 | Up to 23 years | A neural network model using 55 clinical factors achieved 94.48% accuracy in predicting implant survival. |

### References

[1] French, D., et al. (2021). Long term clinical performance of 10 871 dental implants with up to 22 years of follow-up: A private practice study. *Journal of Dentistry*, *111*, 103712. [Available online](https://pmc.ncbi.nlm.nih.gov/articles/PMC8359846/)

[2] Mahendra, J., et al. (2025). Decoding success: A five-year retrospective study of dental Implant survival. *Journal of Prosthodontic Research*, *69*(3), 100-107. [Available online](https://www.sciencedirect.com/science/article/pii/S221242682500140X)

[3] Shahapur, S. G., et al. (2024). Predictive Factors of Dental Implant Failure: A Retrospective Study Using Decision Tree Regression. *Cureus*, *16*(12), e64934. [Available online](https://www.cureus.com/articles/320563-predictive-factors-of-dental-implant-failure-a-retrospective-study-using-decision-tree-regression)

[4] Lyakhov, P. A., et al. (2022). Neural network system for analyzing statistical factors of patients for predicting the survival of dental implants. *Frontiers in Neuroinformatics*, *16*, 1067040. [Available online](https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.1067040/full)