# Machine Learning-Based Approach for Predicting Diabetes Employing Socio-Demographic Characteristics
###### Abstract  
Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages. diabetes; socio-demographic characteristics; machine learning; polydipsia; sudden weight loss 2  
most pronounced in South Asian populations, with the highest prevalence in individuals from Sri Lanka (26.8%), followed by those from Bangladesh (22.2%), Pakistan (19.6%), India (18.3%), and Nepal (16.5%), when compared to non-immigrant populations, which had a prevalence of 11.6% [1]. Diabetes is a chronic illness brought on by either insufficient insulin production by the pancreas or inefficient insulin utilization by the body. There are mainly two types of diabetes, namely type 1 and type 2 diabetes. In addition to that there, are additional types of diabetes such as diabetes mellitus and gestational diabetes [3]. Diabetes is the source of many other deadly diseases. It is a highly potential disease to harm the heart, blood vessels, eyes, kidneys, and nerves. Therefore, it is urgent to predict diabetes among patients. Otherwise, it can cause other diseases in our bodies. We can prevent this dangerous disease by following some lifestyle rules, although this does not discard the risk of developing diabetes. If we can predict diabetes at an early stage, it is possible to control it. Changing their lifestyle and obeying the doctor's suggestion, a patient can experience relief from this disease. Therefore, it is said that predicting diabetes at an early stage is crucial to reducing the mortality rate of this disease. Every year, all the countries around the globe spend a large number of funds on diabetes. A report from the American Diabetes Association (ADA) expresses that the whole world spent USD 245 billion in 2012, and the amount of money increased by USD 82 billion in the following five years.In 2017, this number reached USD 327 billion [4]. Predicting diabetes disease at an early stage can also help reduce these costs. In recent years, numerous studies have looked into predicting diabetes through several machine learning (ML) models. [PERSON] et al. (2021) have shown a neural network (NN) (NN)-based model with an accuracy of 88.6%. In their study, they utilized a dataset obtained from the Pima Indian Diabetes (PID) dataset. Although they built a model with an NN, they did not show the impact of the features on this model in their research and the accuracy of the model was also not good enough [5]. [PERSON] et al. (2020) performed data mining techniques and found that random forest (RF) gave the best results, with a 97.4% accuracy on 10-fold cross-validation and a 99% accuracy on the train-test split. They used a dataset collected through oral interviews from Sylhet Diabetes Hospital patients in Sylhet, Bangladesh. They showed good accuracy, but their dataset was unbalanced.