Improved predictive personalized modelling with the use of Spiking Neural Network system and a case study on stroke occurrences data
- Resource Type
- Conference
- Authors
- Othman, Muhaini; Kasabov, Nikola; Tu, Enmei; Feigin, Valery; Krishnamurthi, Rita; Hou, Zhengguang; Chen, Yixiong; Hu, Jin
- Source
- 2014 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2014 International Joint Conference on. :3197-3204 Jul, 2014
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Neurons
Data models
Reservoirs
Accuracy
Brain modeling
Computer architecture
Predictive models
- Language
- ISSN
- 2161-4393
2161-4407
This paper is a continuation of previous published work by the same authors on Personalized Modelling and Evolving Spiking Neural Network Reservoir architecture (PMeSNNr). The focus is on improvement of predictive modeling methods for the stroke occurrences case study utilizing an enhanced NeuCube architecture. The adaptability of the new architecture leads towards understanding feature correlations that affect the outcome of the study and extracts new knowledge from hidden patterns that reside within the associations. Through this new method, estimation of the earliest time point for stroke prediction is possible. This study also highlighted the improvement from designing a new experimental dataset compared to previous experiments. Comparative experiments were also carried out using conventional machine learning algorithms such as kNN, wkNN, SVM and MLP to prove that our approach can result in much better accuracy level.