Diabetes Prediction Using Machine Learning Algorithm
TL;DR
This study built a diabetes diagnosis system that includes data cleaning, feature selection, and classification and used two types of ensemble learning Decision Tree algorithms, Ada Boost and Random Forest, for feature selection and compared their performance with wrapper-based methods.
Abstract
In this study, we aimed to create a system that uses machine learning to detect and classify diabetes in an e-healthcare setting. We used Ensemble Decision Tree algorithms for selecting important features from a large set of data. Detecting diabetes accurately is a big challenge for researchers, especially in e-healthcare environments. Many existing systems have problems like slow processing and low accuracy. To fix these issues, we built a diabetes diagnosis system that includes data cleaning, feature selection, and classification. We tested the system using methods to check its effectiveness. We used a filter method based on the Decision Tree algorithm to choose the most important features. We also used two types of ensemble learning Decision Tree algorithms, Ada Boost and Random Forest, for feature selection and compared their performance with wrapper-based methods. The Decision Tree classifier was used to separate healthy individuals from those with diabetes. The results showed that using the selected features improved the model's classification performance and achieved the best accuracy. The system also performed better than previous methods due to the combination of different feature sets.