Data mining in telemedicine
To date, the field of telemedicine is at a critical standpoint and faces a wide variety of challenges. Voluminous data are generated through the interaction among the telemedicine stakeholders, which are ever increasing. It is well conjectured that the successful implementation of telemedicine largely depends on the effective and efficient knowledge extraction from this available data cloud. However, due to lack of proper integration of the data mining techniques, the stakeholders are not getting the full-fledged benefit from this promising platform. Considering the aforementioned fact, this book chapter provides a contrivance to integrate data mining techniques into telemedicine connecting all the stakeholders into a single podium using data engine. It illustrates the prospects of different data mining techniques and their integration for telemedicine. These techniques combine all the basic classification and clustering method including the state-of-the-art artificial neural network (ANN) and deep learning procedure for disease prediction. Two case studies, heart diseases, and breast cancer prediction have been demonstrated applications of the integrated data mining engine.
Rahman, M. F., Wen, Y., Xu, H., Tseng, T.-L. B., & Akundi, S. (2020). Data mining in telemedicine. In Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications (pp. 103–131). IET Digital Library. DOI:10.1049/PBHE023E_ch6.
Advances in Telemedicine for Health Monitoring: Technologies, Design and Applications