Artificial intelligence in dentistry: Harnessing big data to predict oral cancer survival

Figure 1 Feature selection using random forest. CS: Coding system; ICD-O-3: International Classification of Diseases for Oncology, 3rd ed; WHO: World Health Organization; AJCC: American Joint Committee on Cancer; SEER: Surveillance, Epidemiology, and End Results; LN: Lymph node.

Figure 2 Cross-validation score change for selecting optimal number of features. LN: Lymph node; SEER: Surveillance, Epidemiology, and End Results; AJCC: American Joint Committee on Cancer; CS: Coding system; ICD-O-3: International Classification of Diseases for Oncology, 3rd ed; WHO: World Health Organization.

Figure 3 Boxplots of sample characteristics. CS: Coding system.

Figure 4 Survival months shows strong linear relation with several variables: Age of diagnosis, year of diagnosis, month of diagnosis, and site recode ICD-O-3/WHO 2008. ICD-O-3: International Classification of Diseases for Oncology, 3rd ed; WHO: World Health Organization.

Figure 5 Machine learning model feature importance. ICD-O-3: International Classification of Diseases for Oncology, 3rd ed; WHO: World Health Organization; CS: Coding system; AJCC: American Joint Committee on Cancer.

Figure 6 Prediction comparison among different models. Patient index refers to the rank after sorting by survival months. Actual: The actual survival outcome; XGB: Extreme gradient boosting.