A survey on diabetes risk prediction using machine learning approaches

Background: Diabetes mellitus (DM) is a chronic condition that can lead to a variety of consequences. Diabetes is a condition that is caused by factors such as age, lack of exercise, sedentary lifestyle, family history of diabetes, high blood pressure, depression and stress, poor food, and so on. Diabetics are at a higher risk of developing diseases such as heart disease, nerve damage (diabetic neuropathy), eye problems (diabetic retinopathy), kidney disease (diabetic nephropathy), stroke, and so on. According to the International Diabetes Federation, 382 million people worldwide suffer from diabetes. By 2035, this number will have risen to 592 million. Every day, a large number of people become victims, and many are ignorant whether they have it or not. It primarily affects individuals between the ages of 25 and 74 years. If diabetes is left untreated and undiagnosed, it can lead to a slew of complications. The emergence of machine learning approaches, on the other hand, solves this crucial issue. Aims and Objectives: The aim was to study the DM and analyze how machine learning algorithms are used to identify the diabetes mellitus at an early stage, which is one of the most serious metabolic disorders in the world today. Methods and Materials: Data was obtained from databases such as Pubmed, IEEE xplore, and INSPEC,and from other secondary sources and primary sources in which methods based on machine learning approaches used in healthcare to predict diabetes at an early stage are reported. Results: After surveying various research papers, it was found that machine learning classification algorithms like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) etc shows the best accuracy for predicting diabetes at an early stage. Conclusion: Early detection of diabetes is critical for effective therapy. Many people have no idea whether or not they have it. The full assessment of Machine learning approaches for early diabetes prediction and how to apply a variety of supervised and unsupervised machine learning algorithms to the dataset to achieve the best accuracy are addressed in this paper.. Furthermore, the work will be expanded and refined to create a more precise and general predictive model for diabetes risk prediction at an early stage. Different metrics can be used to assess performance and for accurate diabetic diagnosis.