## 2 Background and Related Work
### ML-Based Methods  
[PERSON] et al. [28] applied and evaluated four ML algorithms (decision tree, K-nearest neighbors, artificial neural network, and deep neural network) to predict patients with or without type 2 diabetes mellitus. These techniques were trained and tested on two diabetes databases: One obtained from Frankfurt hospital (Germany), and the other one,the openly available, well-known Pima Indian dataset ([[https://www.kaggle.com/uciml/Pima-indians-diabetes-database](https://www.kaggle.com/uciml/Pima-indians-diabetes-database)]([https://www.kaggle.com/uciml/Pima-indians-diabetes-database](https://www.kaggle.com/uciml/Pima-indians-diabetes-database))). These datasets contained the same features composed of risk factors and some clinical data such as the number of pregnancies, glucose levels, blood pressure, skin thickness, insulin, BMI (Body Mass Index), age, and diabetes pedigree function. The results compared using different similarity metrics give a classification accuracy of more than 90% and up to 100% in some cases. Similarly, many other approaches trained their models on similar features. For example, in [15; 16; 17; 18; 19; 20]) the authors used the Pima Indian diabetes dataset by modifying the preprocessing steps, applying different algorithms and adjusting their hyperparameters to generate improved results. The limiting factor of these approaches is the inclusion of some features like skin thickness, insulin, and diabetes pedigree function, which are generally not available or recorded. Moreover, factors like skin thickness may result in the classification based on ethnic function, thus, preventing a wide-range applicability of the approach. [PERSON] et al. [29] built a predictive model to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results. Their data included the patient features age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein. They built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques achieving good sensitivity results. But the authors did not mention their performance in accuracy or specificity, which usually has better sensitivity as a trade-off. Thus, their performance cannot be generalized. Like this, many research works have compared the performance of several machine learning using the selected metrics, while a different metric may give a poor performance on the same model. Many other approaches for diabetes classification concluded that a certain type of algorithms can give better results for prediction without considering the issue of the generality of their models [30; 31]. A number of other studies have used the National Health and Nutrition Examination Survey (NHANES) ([[https://wwwn.cdc.gov/nchs/nhanes/](https://wwwn.cdc.gov/nchs/nhanes/)]([https://wwwn.cdc.gov/nchs/nhanes/](https://wwwn.cdc.gov/nchs/nhanes/))) from the US Center for Disease Control (CDC) for the prediction of diabetes or other diseases. The NHANES data was initiated in 1999 and is growing every year in the number of records as well as the variables it considers in its surveys. These studies, while utilizing the main NHANES dataset, use some subset of variables for disease prediction or classification tasks. For example, [PERSON] et al. [32] identified 14 important variables--age, weight, height, BMI, gender, race and ethnicity, family history, waist circumference, hypertension, physical activity, smoking, alcohol use, education, and household income--for training their machine learning models. Using two different classification schemes, they achieved 83.5% and 73.2% results for the area under the Receiver Operating Characteristic (ROC) curve. [PERSON] and [PERSON] [33] added two more variables--cholesterol and leg length--in their analysis. By applying an ensemble model using the output of five classification algorithms they were able to predict the onset of diabetes with an AUC (Area Under Curve) of 83.4%.