# Artificial Intelligence Algorithms for Treatment of Diabetes
## 1 Introduction  
Artificial intelligence (AI) has provided new powerful approaches to addressing various problems in medicine [1, 2]. This work focuses on machine learning (ML) applications in diagnosing and treating diabetes. Since most ML techniques are data-driven, the source, type, and quality of data play an important role in the performance, accuracy, and reliability of the developed ML models. The data used can be subjective, objective, or a combination of both [3, 4]. Subjective sources of data include diaries kept by the individual, her/his responses to questionnaires, and self-reported information such as meals and physical activities entered in various apps that collect such data. Objective data are collected by devices that record measurements of numerous physiological variables and test results reported. Challenges to objective data quality include accuracy of measurements, effects of measurement noise and artifacts, missing values, and outliers in data. Under or overestimation of food consumed, variations in the level of stress or pain perceived by the patient, and forgetting entries to diaries are common in subjective data. When data are collected for various classes of events, the balance in the number of samples in each class can also influence or bias the results of the ML algorithms. This manuscript will first discuss data preprocessing methods to reduce the influence of imbalances and faults in data. Then it will illustrate the use of ML in addressing various challenges in the treatment of diabetes. Diabetes, a chronic disease that affects one out of every eleven people around the world, will be used to illustrate the ML applications for detection, classification, andprediction problems. Diabetes has a large impact on the quality of life of people with diabetes and a significant financial burden on individuals and society [5]. Type 1 diabetes (T1D) and Type 2 diabetes (T2D) are two major types of diabetes [6]. T1D is caused by an absolute insulin deficiency, a result of the loss of insulin-producing beta cells of the pancreas. T2D is characterized by the ineffective use of insulin in the body and defects in insulin secretion that lead to relative insulin deficiency in relation to the increased insulin requirements imposed by the insulin-resistant state. While the _cure_ of diabetes is the objective of many active research programs, there is no cure approved to date to eliminate diabetes [7]. Many activities focus on improving the _treatment_ of diabetes using various classes of pharmacological drugs and medical devices that can help improve the regulation of blood glucose concentrations (BGC) in people with diabetes [8; 9; 10]. The role of ML in the classification of the characteristics of T2D, and the detection of the patterns of the daily behavior of people with T1D will be discussed in this publication. The ML problem for T2D pertains to the development of a decision support system that recommends the most effective treatments (drugs prescribed and lifestyle changes) in people with T2D. The ML algorithm can suggest personalized treatment intervention strategies depending on the characteristics of the subject with T2D and the state of the progressive disease, which can lead to either remission (in the early stages of T2D) or treatment of T2D [11; 12; 13; 14; 15; 16]. The use of ML in the treatment of T1D focuses on capturing the patterns of the daily behavior of people with T1D [17; 18] and to use this information in enhancing the regulation of their BGC and automated insulin delivery [19]. While our focus is on diabetes, the approaches and applications can be extended to many other chronic diseases. The application of AI algorithms also differs between T1D and T2D, because of distinct timescales and frequencies of decision-making in the treatment of T1D and T2D. T1D necessitates frequent real-time decisions on insulin dosing, which requires AI algorithms to infer patterns from historical data and analyze in real-time multiple streaming data sources.