Manuscript received on January 15, 2021. | Revised Manuscript received on February 09, 2021. | Manuscript published on March 30, 2021. | PP: 1-5 | Volume-9 Issue-6, March 2021. | Retrieval Number: 100.1/ijrte.E5255019521 | DOI: 10.35940/ijrte.E5255.039621
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Diabetes has become a famous and lethal disease among the low and medium-income countries. People could not overcome this deadly abnormal condition due to the current lifestyle, food habit and the genetic transmittance. Medical practitioners provide advice to prevent the diabetic condition and medications to control as this disease does not have a permanent cure. However, the detection of the disease is being a tidy process and deployment of machine learning predictive models to conduct smart diagnosis/detection is vital in the healthcare domain nowadays. Though several machine learning models were built in this regard, deploying a Deep Neural Network seems less focused. Therefore, a Deep Neural Network model was built with the support of complete preprocessing, class balancing, normalization, feature selection process and hyper-parameter tuning using the cross-validated searching technique. The model achieved 88% of accuracy and 0.88 ROC score and standing out as a promising predictive model in diagnosing/detecting diabetes.
Keywords: Diabetes, healthcare, predictive modelling, deep neural network, optimization.