Diabetes disease prediction using decision tree for feature selection

Abstract

In this paper more than one approaches are evaluated to optimise machine learning models for diabetes disease diagnosis. The main goal is to sort the medical data computation and choose the most suitable parameters to construct a faster and more perfect model using feature selection. Reducing the number of features to construct a model could direct to more useful machine learning algorithms which helps the doctors to focus on what are the most significant assessment to take into story. Feature selection is one of the process in machine learning which choose a subset of topical features namely variables for construction of models. In this research paper we use three feature selection techniques like Recursive Feature Elimination (RFE), Genetic Algorithm (GA) and Burota Package. After using feature selection at the end we use Decision Tree to predict the diagnosis Diabetes using a dataset named Pima Indian Diabetes Dataset and verify the performance of result model.

An Integrated Approach for Diabetes Detection Using Fisher Score Feature Selection and Capsule Network

“…Using feature selection techniques such as recursive feature elimination [30], the genetic algorithm (GA) [31], and the Boruta package [32], the performance of decision tree classifiers was enhanced. After applying the feature selection technique, the PID dataset was utilized to evaluate the model's performance [33]. Random forest is one of the most recent and fruitful findings in decision tree learning research.…”