The Prediction of Diseases using Rough Set Theory with Recurrent Neural Network in Big Data Analytics

Abstract

In a modern life, early healthcare prediction plays an important role to prevent the loss of life caused by prediction delays in treatment. Nowadays, the researchers focused on the Big data analysis, which is used to identify the future health status and provides an efficient way to overcome the issues in early prediction. Many researches are going on predictive analytics using machine learning techniques to provide a better decision making. Big data analysis provides great opportunities to predict future health status from health parameters and provide best outcomes. However, the data classification is one of the major challenging tasks due to noisy data or missing data in the dataset. Feature selection techniques play an important role in the classification process by removing irrelevant features from the extracted data. In this research work, the Rough Set Theory (RST) technique is used to select the most relevant features, which helps to provide the efficient classification of medical data and disease detection. The selected features are given as input to the Recurrent Neural Network (RNN) technique for disease prediction. The proposed method is also called as RST-RNN, where the experiments are carried out on the UCI machine learning repository dataset in terms of accuracy, f-measure, sensitivity and specificity. The results showed that the RST-RNN method achieved accuracy of 98.57%, where the existing Support Vector Machine (SVM) achieved 90.57% accuracy and Naive Bayes (NB) achieved 97.36% accuracy for heart disease dataset.

[Retracted] Analysis of Sports Performance Prediction Model Based on GA‐BP Neural Network Algorithm

“…e information transmission of BP neural network algorithm is composed of two transmission modes alternately. For example, formula (2) shows the positive propagation process:…”

“…In the GA algorithm, the middle population is represented as v jt , and 2 is defined as the mean square error between the actual output and the ideal output. at is to say, the smaller the value is, the more reliable the output is and the smaller the accuracy of the prediction of sports performance is.…”

“…erefore, a scientific, reasonable, and targeted training plan is needed in the process of training [2].…”

[Retracted] Sports Training System Based on Convolutional Neural Networks and Data Mining

“…Luan studied the coupling relationship between the sphere's motion trajectory and the athlete's gait and finally combined with the convolutional neural network algorithm and designed experiments to prove the role of this method in improving the safety of basketball [ 12 ]. Talasila et al have studied the factors that have a greater impact on the sports training process by combining multiple neural network analysis strategies and selected superresolution factors to establish a functional relationship between gait recognition and different dynamic performance [ 13 ]. Hu et al have found that sports have a problem of low resolution during training.…”

[Retracted] Determination of Bridge Prestress Loss under Fatigue Load Based on PSO‐BP Neural Network

“…In addition, in the modeling and analysis of the bearing capacity of bridge buildings, the advantages of the prestressed design of bridges under fatigue load based on cloud computing technology and intelligent algorithm are more obvious. e development of these technologies also provides opportunities for the research on the combination of the prestressed design and ultimate load performance of the bridge under the action of building stability and fatigue load [1]. erefore, the mechanical performance analysis and bearing capacity design of the bridge prestressed under fatigue load have become an important index to judge the advanced level of bridge construction field, and there have been studies on the application of neural network algorithms to the determination of prestress loss [2].…”