DIABETES DISEASE PREDICTION USING MACHINE LEARNING ENSEMBLE METHOD

Abstract

Machine learning incorporates AI, and is used to solve many problems in data science. The machine reads patterns from existing databases, and then inserts them into an unknown database to predict the outcome. Classification can be a powerful machine learning method commonly used for prediction. Some classification algorithms provide satisfactory accuracy, while others provide restricted accuracy. This paper examines a method called ensemble classification, which is often used to improve the accuracy of weak algorithms by combining multiple categories. Tests for this tool are performed using a diabetic database. A comparative analytical approach was performed to find out how the ensemble process is often used to improve diabetes prognosis. The goal of this paper is not only to increase the accuracy of weak classification algorithms, but also to implement an algorithm on a medical database, to demonstrate its ability to detect the disease at an early age. The results of the study indicate that integrated strategies, such as the random forest, are effective in increasing the predictive accuracy of weak classifiers, and have shown satisfactory effectiveness in identifying the risks of diabetes. A seven-point increase in the accuracy of the weak classifiers was achieved with the help of an ensemble classification.