A Novel Random Forest-Based Algorithm for Diabetes Diagnosis

This chapter genuinely addresses the crucial need for the diagnosis of diabetes, since the techniques that are currently in use today are still lacking in both efficiency and accuracy. For the present investigation, we have chosen to use the Random Forest classifier as our model. The Random Forest algorithm is an ensemble learning method that creates a lot of decision trees during training and produces a class that is either the mode of the classes for classification or the mean forecast of the individual trees for regression. The investigation builds and compares different intelligent systems based on multilayer algorithms using the data set that Kare published. Such variables include levels of blood glucose, levels of HbA1c, smoking histories, cardiovascular disease, hypertension, age, gender, and body mass index (BMI). Furthermore, to offering insights into the trends and patterns in diabetes risk, this thorough analysis will lay the groundwork for future studies. In particular, studies can be conducted to better understand how these factors interact and affect the development and course of diabetes, which is essential information for enhancing patient care and results in this increasingly important field of medicine.