Binary Classification of Diabetes in Pima Indian Dataset: A Deep Learning Perspective

Abstract

Abstract: In this study, we develop into the application of deep learning methodologies for diabetes prediction utilizing the Pima Indian dataset. Employing Keras with Theano as the backend, we establish a binary classification model to effectively forecast the presence or absence of diabetes in individuals. Our research aims to enhance the precision and reliability of diabetes diagnosis, ultimately contributing to improved healthcare decision-making. Our investigation leverages Keras, a high-level neural networks API, in conjunction with Theano, to conduct binary classification on the Pima Indian diabetes dataset. Our study provides valuable insights into the field of medical data analysis, showcasing the effectiveness of deep learning techniques in advancing diagnostic tools for proactive healthcare management. Diabetes mellitus, a prevalent chronic disease globally, necessitates the development of a system for early type 2 diabetes mellitus (T2DM) diagnosis. Multiple machine learning and data mining techniques, including ANN, SVM, KNN, decision trees, and Extreme Learning Machines, have emerged and been employed as aids in diabetes detection. Consequently, we introduce Deep Learning, a subfield of machine learning, which can effectively handle smaller datasets through efficient data processing techniques. This paper presents an in-depth review of Diabetic Retinopathy, covering its features, causes, various ML models, DL models, challenges, comparisons, and future directions for early DR detection. Diabetes mellitus is a global health concern with a rapidly increasing prevalence. In this context, machine learning technologies prove invaluable for early disease identification and diagnosis. The focus of this study is to identify the most effective ML algorithm for diabetes prediction.