An Oversampling Technique for Handling Imbalanced Data in Patients with Metabolic Syndrome and Periodontitis

Abstract

Objectives: Periodontitis has been suggested to be associated with several systemic diseases and conditions including obesity, metabolic syndrome, diabetes, chronic renal disease, respiratory disorders, and cardiovascular diseases. Metabolic syndrome (MetS) is a collection of impairment and is a risk factor for type 2 diabetes and cardiovascular disease. Our study is aimed to handle MetS unbalanced data using the synthetic minority over-sampling technique (SMOTE) to increase accuracy and reliability. Materials and Methods: Six metabolic syndrome patients and 26 systemically healthy subjects with periodontitis were recruited in this study. Clinical parameters (Plaque index (PI), gingival index (GI), probing pocket depth (PPD), clinical attachment loss (CAL), and bleeding on probing (BOP)) were obtained, smoking status and body-mass index (BMI), systemic diseases, fasting glucose levels, hemoglobin A1c (HbA1c) levels and serum advanced glycation end-products (AGE) levels were recorded by one examiner. First, the data was pre-processed by removing missing values, outliers and normalizing the data. Then, SMOTE technique was used to oversample the minority class. SMOTE works by creating synthetic data points that are similar to the existing minority class instances. The experimental dataset included numerous machine learning algorithms and assessed accuracy using both pre- and post-oversampling methods. Results: Our findings suggest that by increasing the sample size of a study, researchers can gain more accurate and reliable results. This is especially important when studying a population with a lower sample size, as the results may be skewed. Conclusion: SMOTE may result in over fitting on numerous copies of minority class samples.