Pregnancy Period Diabetic and Blood Pressure Predictive Analysis using HCNN LSTM

Abstract

Diabetes has transformed into the worldwide diseases and can occur for all age groups irrespective of their gender. Unlike other diseases, Diabetes needs continuous monitoring as it leads to much adverse effect on functioning of human body. Especially, the diabetes that occurs in female during the pregnancy had its impact over the mother along with their infant before its birth. Many studies showed early prediction can prevent and delimit the challenges that were posed by diabetes among pregnant women. Several health care prediction models often suffer from inconsistencies in data and feature selection that reduce the prediction performance. In the present work, we had proposed the novel Health Care Neural Network-Long Short Term Memory (HCNN-LSTM) to predict the Pregnancy Period Diabetic and Blood Pressure. The Pima Indian diabetes dataset was employed construct the proposed prediction model to predict the patient as diabetic and non-diabetic. For the purpose of comparison, the decision tree, random forest and Navies’ Bayes algorithm are implemented for classification. From the analysis, it was evident that the proposed HCNN-LSTM showed optimum values on performance metrics than the other classifiers. The proposed work can be expanded considering several features of diabetic prediction in future.