AI Summary & Key Takeaways
AI InsightThe classification tree method showed that the most significant factors affecting halitosis were oral health problems followed by smoking status.
📌 3 Key Takeaways
Abstract
Decision trees are data mining techniques for extracting hidden knowledge from large databases. This study was performed to establish the risk factors associated with halitosis by applying a decision tree model in a Turkish population and examining the interactions between these factors. We obtained data from a total of 1.290 patients, consisting of 645 patients with halitosis and 645 healthy controls. The subjects’ demographic characteristics, smoking status, alcohol intake, medical history and medications were assessed. The presence of potential intraoral causes of halitosis was determined by investigating perceived oral health problems such as caries, periodontal diseases, tongue coating, and oral cavity pathologies. Halitosis level was evaluated using an organoleptic scale. All data were subjected to classification tree analyses. Halitosis was significantly more common in patients with (80.9%) than without (20.7%) oral health problems (P < 0.001). Halitosis was significantly less common in non-smokers without oral health problems than in smokers with oral health problems (14.5%; P < .001). Halitosis was evident in all patients with oral health problems, smokers, and those with respiratory diseases (100%). The effects of systemic diseases on halitosis were significant in non-smokers without oral health problems (P < 0.05). Respiratory conditions showed significant effects on halitosis in smokers with oral health problems (P < 0.01). We developed a decision tree model to identify risk factors associated with halitosis. The classification tree method showed that the most significant factors affecting halitosis were oral health problems followed by smoking status.