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25 posts tagged with "paper"

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· One min read

Abstract

Radio frequency identification (RFID) technology can be utilized to monitor tagged product movements and directions for the purpose of inventory management. It is important for RFID gate to identify the several RFID readings such as movement type and direction as well as the static tags (tags that accidentally read by the reader). In this study, random forest (RF) method is utilized to detect the movement type and direction of RFID passive tags. The input features are derived from received signal strength (RSS) and timestamp of tags. The result showed that machine learning models successfully distinguish direction and movement type of tag. In addition, proposed model based on random forest generated accuracy as much as 98.39% and was significantly superior to the other models considered.



Published in: IEEE Xplore
DOI: 10.1109/ICST47872.2019.9166196

· 2 min read

Abstract

Heart disease, one of the major causes of mortality worldwide, can be mitigated by early heart disease diagnosis. A clinical decision support system (CDSS) can be used to diagnose the subjects’ heart disease status earlier. This study proposes an effective heart disease prediction model (HDPM) for a CDSS which consists of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and eliminate the outliers, a hybrid Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) to balance the training data distribution and XGBoost to predict heart disease. Two publicly available datasets (Statlog and Cleveland) were used to build the model and compare the results with those of other models (naive bayes (NB), logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), and random forest (RF)) and of previous study results. The results revealed that the proposed model outperformed other models and previous study results by achieving accuracies of 95.90% and 98.40% for Statlog and Cleveland datasets, respectively. In addition, we designed and developed the prototype of the Heart Disease CDSS (HDCDSS) to help doctors/clinicians diagnose the patients’/subjects’ heart disease status based on their current condition. Therefore, early treatment could be conducted to prevent the deaths caused by late heart disease diagnosis.

Published in: IEEE Access
DOI: 10.1109/ACCESS.2020.3010511

· One min read

Abstract

Predicting future blood glucose (BG) level for diabetic patients will help them to avoid critical conditions in the future. This study proposed Extreme Gradient Boosting (XGBoost), an ensemble learning model to predict the future blood glucose value of diabetic patients. The clinical dataset of Type 1 Diabetes (T1D) patients was utilized and the prediction models were generated to predict future BG of 30 and 60 minutes ahead of time. The prediction models have been tested tofive children who develop T1D and showed that BG prediction model based on XGBoost outperformed other models, with average of Root Mean Square Error (RMSE) are 23.219 mg/dL and 35.800 mg/dL for prediction horizon (PH) 30 and 60 minutes respectively. In addition, the result showed that by utilizing statistical-based features as additional attributes, most of the performance of predictions model were increased.

Published in: IOP Conference Series: Materials Science and Engineering
DOI: 10.1088/1757-899X/803/1/012012