Monitoring of Diabetes with Data Mining via CART Method

Disease Management Programs are beginning to encompass providers across the healthcare continuum, including home health care. The premise behind disease management is that coordinated, evidence-based interventions can be applied to the care of patients with specific high-cost, high-volume chronic conditions, resulting in improved clinical outcomes and lower overall costs. The paper presents an approach to designing a platform to enhance effectiveness and efficiency of health monitoring using DM for early detection of any worsening in a patient’s condition. In this article, we briefly describe the diabetic monitoring platform we designed and realized which supports Diabetes (D) diagnosis assessment offering functions of DM based on the CART method. The work also gives a description of constructing a decision tree for diabetes diagnostics and shows how to use it as a basis for generating knowledge base rules. The system developed achieved accuracy and a precision of 96.39% and 100.00% in detecting Diabetes These preliminary results were achieved on public databases of signals to improve their reproducibility. Finally, the article also outlines how an intelligent diagnostic system works. Clinical trials involving local patients are still running and will require longer