期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2021
卷号：3
期号：5
页码：939-943
DOI：10.35629/5252-0305828832
语种：English
出版社：IJAEM JOURNAL
摘要：Diabetes is a metabolic disease affecting a multitude of people worldwide. Its incidence rates are increasing alarmingly every year. If untreated, diabetes-related complications in many vital organs of the body may turn fatal. Early detection of diabetes is very important for timely treatment which can stop the disease progressing to such complications. An intelligent predictive model using deep learning is proposed to predict the patient risk factor and severity of diabetics using conditional data set. The model involves deep learning in the form of a deep neural network which helps to apply predictive analytics on the diabetes data set to obtain optimal results. The existing predictive models are used to predict the disease whether it is normal or not based on the data which is processed. In this project firstly, a feature selection algorithm is run for the selection process. Secondly, the deep learning model has a deep neural network which employs back propagation neural network as a basic unit to analyses the data by assigning weights to the each branch of the neural network. This deep neural network, coded on python, will help to obtain numeric results on the severity and the risk factor of the diabetics in the data set. At the end, can provide prescription based results for disease diagnosis based on Pima Indians diabetes. This will help to predict diabetes with much more precision as shown by the results obtained.
