Binary Classification of Diabetes on PIMA In...

Binary Classification of Diabetes on PIMA Indian Dataset: A Deep Learning Perspective

Author Name : Moumita Dey, Akhand Pratap Singh, Shiwanshi, Shiva Rao, Waquif Akhtar

ABSTRACT The diabetes dataset is a binary classification problem where it needs to be analysed whether a patient is suffering from the disease or not on the basis of many available features in the dataset. Different methods and procedures of cleaning the data, feature extraction, feature engineering and algorithms to predict the onset of diabetes are used based for diagnostic measure on Pima Indians Diabetes Dataset. Our research aims to enhance the precision and reliability of diabetes diagnosis, ultimately contributing to improved healthcare decision-making. Diabetes mellitus, a widespread chronic disease worldwide, under- scores the need for a system to diagnose type 2 diabetes mellitus (T2DM) early. Various machine learning and data mining techniques, such as ANN, SVM, Linear Regression, decision trees, and Extreme Learning Machines, have been developed and utilized to assist 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. The focus of this study is to identify the most effective ML algorithm for diabetes prediction.