Diabetes Detection Using Machine Learning Algorithms

Diabetes is a serious illness. Predicting this disease in a timely manner is necessary to avoid severe side effects. Current medical practise dictates that a patient undergoes a battery of tests in order to obtain the information necessary for diagnosis, after which treatment is administered based on the diagnosis. However, in many cases, the early stages go undetected, and it is quite difficult for physicians to diagnose due to the interdependence of numerous factors. A single parameter is commonly inadequate for the accurate diagnosis of diabetes and may lead to erroneous decisions. To accurately forecast diabetes at an early stage, multiple criteria must be combined. This study proposes the development of an early diabetes detection model. The model will not only be more accurate than humans, but it will also reduce the workload of medical professionals.