Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset.
Patient admitted with acute decompensated heart failure (ADHF) facing with high risk of mortality where 30 day mortality rates are reaching 10%. Identifying patient with high and low risk of mortality could improve clinical outcomes and hospital resources allocation. This paper proposed the use of artificial neural network to predict mortality for the patient admitted with ADHF. Results show that artificial neural network can predict mortality for ADHF patient with good prediction accuracy of 94.73% accuracy for training dataset and 91.65% for test dataset.
A new dynamic channel allocation scheme (DCAS) has been proposed for mobile satellite systems with the aim of improving the utilization of the network resources by reducing the handoff call dropping probability (HODP) while guaranteeing a certain quality of service for the new call blocking probability (NCBP). The arriving calls are given channels based on their priority. The handoff calls have higher priority compared to the new calls, and real time traffic calls have higher priority over non real rime calls. The DCAS is then combined with the shortest path routing for a adaptive channel management scheme.
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