A Wide Survey and Current Approaches for Diabetes Disease Prediction Using Data Analytics and Machine Learning Techniques

Abstract

Diabetes is a chronic disease characterized by a high amount of glucose in the blood and can
cause too many complications also in the body, such as internal organ failure, retinopathy, and
neuropathy. According to the predictions made by WHO, the figure may reach approximately
642 million by 2040, which means one in a ten may suffer from diabetes due to unhealthy
lifestyle and lack of exercise. Clinical data on diabetes patients are readily available in many
countries and research directories. These data are not necessarily in the same format or do not
contain error-free or clear information about diabetes. These incomprehensive and nonhomogenous
data are a great source of conflict for the practitioner and the research
communities. Diabetes diagnosis falls under the data classification problem and so much
literature exists in this subject area. Artificial intelligence and data analytics techniques are
effective solutions that merit the attention of any researcher. The essential characteristics of
current intelligent solutions for the recognition of diabetic disease are identified, along with
key trends, in this paper's assessment of recent studies in the field, many of which use machine
learning or statistics-based techniques.