scholarly journals Performance Evaluation of Naive Bayes and Back Propagation Neural Network classifiers in gestational Diabetes Mellitus Classification

2020 ◽  
Vol 8 (10) ◽  
pp. 7165-7172
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Performance Evaluation ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Naive Bayes ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Naïve Bayes ◽  
Neural Network Classifiers

scholarly journals A Framework for Prediction of Household Energy Consumption Using Feed Forward Back Propagation Neural Network

Technologies ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 30 ◽  
Author(s):  
Muhammad Fayaz ◽  
Habib Shah ◽  
Ali Aseere ◽  
Wali Mashwani ◽  
Abdul Shah
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Energy Consumption ◽  
Data Acquisition ◽  
Residential Buildings ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Statistical Moments ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Energy is considered the most costly and scarce resource, and demand for it is increasing daily. Globally, a significant amount of energy is consumed in residential buildings, i.e., 30–40% of total energy consumption. An active energy prediction system is highly desirable for efficient energy production and utilization. In this paper, we have proposed a methodology to predict short-term energy consumption in a residential building. The proposed methodology consisted of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For experimental analysis, real data collected from 4 multi-storied buildings situated in Seoul, South Korea, has been used. The collected data is provided as input to the data acquisition layer. In the pre-processing layer afterwards, several data cleaning and preprocessing schemes are applied to the input data for the removal of abnormalities. Preprocessing further consisted of two processes, namely the computation of statistical moments (mean, variance, skewness, and kurtosis) and data normalization. In the prediction layer, the feed forward back propagation neural network has been used on normalized data and data with statistical moments. In the performance evaluation layer, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) have been used to measure the performance of the proposed approach. The average values for data with statistical moments of MAE, MAPE, and RMSE are 4.3266, 11.9617, and 5.4625 respectively. These values of the statistical measures for data with statistical moments are less as compared to simple data and normalized data which indicates that the performance of the feed forward back propagation neural network (FFBPNN) on data with statistical moments is better when compared to simple data and normalized data.

scholarly journals Early Gestational Diabetes Detection Using Neural Network

2021 ◽  
Vol 18 ◽  
pp. 1-7
Author(s):  
Tanzina Rahman Hera ◽  
Md. Ashikur Rahman Khan ◽  
Nishu Nath
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Early Detection ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Cost Effective ◽  
First Trimester ◽  
Develop Type ◽  
Fetal Complications ◽  
Artificial Neural Network Ann

Gestational Diabetes Mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy. Fifty percent of GDM patients develop type 2 Diabetes in next twenty years and as well as the newborn can also be affected by diabetes in their lifetime. So the long term complications for both the mother and the child cannot be ignored. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity in the present scenario. In developing country like Bangladesh, early detection and prevention is not cost effective and usually troublesome. So, there is an urgent need for a well-designed method for the detection of gestational diabetes mellitus. The purpose of this study is to predict the GDM in the first trimester. This research presents and compares some Artificial Neural Network (ANN) models on the early detection of Gestational diabetes mellitus and chooses the best neural network model among them to detect GDM early.

Abstract #1012: Gestational Diabetes Mellitus (GDM) in Bangladesh

2016 ◽  
Vol 22 ◽  
pp. 233-234
Author(s):  
Md Abdullah Mamun ◽  
Subrina Jesmin ◽  
Md. Arifur Rahman ◽  
Md Majedul Islam ◽  
Farzana Sohael ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus
Sign in / Sign up

Export Citation Format

Share Document