Classification of Diabetes using Deep Learning
Santosh Kumar, Bharat Bhusan, Debabrata Singh, Dilip Kumar Choubey
Abstract
Deep Learning (DL) is a research area that has flourished significantly in recent years and has shown remarkable potential for artificial intelligence in the field of medical applications. We have implemented the DL algorithm for the diabetes classification. This paper applied the Multi-Layer Feed Forward Neural Networks (MLFNN) for the diabetes classification on the Pima Indian Diabetes datasets. Furthermore, various activation functions, learning algorithms, and techniques to handle missing values are considered to enhance the classification accuracy of the diabetes dataset. Finally, the outcomes of experiments are compared with two machine learning algorithms like Nave Bayes and Random Forest. The achieved classification accuracy by MLFNN (84.17%) is the best of all the other classifiers.