A deep learning approach based on convolutional LSTM for detecting diabetes.

Computational Biology and Chemistry
Motiur RahmanIndrajit Saha

Abstract

Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified and untreated, then it could be very deadliest. One can lead a healthy life with proper treatment if the presence of diabetes can be detected at an early stage. When the conventional process of detecting diabetes is tedious, there is a need of an automated system for identifying diabetes from the clinical and physical data. In this study, we developed a novel diabetes classifying model based on Convolutional Long Short-term Memory (Conv-LSTM) that was not applied yet in this regard. We applied another three popular models such as Convolutional Neural Network (CNN), Traditional LSTM (T-LSTM), and CNN-LSTM and compared the performance with our developed model over the Pima Indians Diabetes Database (PIDD). Significant features were extracted from the dataset using Boruta algorithm that returned glucose, BMI, insulin, blood pressure, and age as important features for classifying diabetes patients more accurately. We performed hyperparameter optimization using Grid Search algorithm in order to find the optimal parameters for the applied models. Initi...Continue Reading

References

May 18, 2000·Journal of Pharmaceutical and Biomedical Analysis·S Agatonovic-Kustrin, R Beresford
Oct 19, 2017·Briefings in Bioinformatics·Frauke DegenhardtSilke Szymczak
Jun 24, 2018·Insights Into Imaging·Rikiya YamashitaKaori Togashi

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