Abstract
Patients are required to be observed and treated continually in some emergency situations. However, due to time constraints, visiting the hospital to execute such tasks is challenging. This can be achieved using a remote healthcare monitoring system. The proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoT sensors are employed, which collect real healthcare data. These data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. This algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. Next, the optimum feature selection technique for extraction and selection of features is illustrated. A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. The proposed system's performance is finally examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring.
Highlights
Healthcare monitoring and diagnosis of health became a vital part of the healthcare sector
Data storage & Cloud computing process techniques beginning from the description of wearable sensors to the recent trends in smart health. e review concludes that machine learning and artificial intelligence play a vital role in healthcare systems but their applications need cloud services’ assistance
E key contribution of the proposed work is given as follows: (i) Initially, an Improved Pigeon Optimization (IPO) algorithm or alternate data science technique is introduced for data compilation and data storage in the cloud for enhancing the speed of forecasting (ii) en the optimal features are described for choosing and gathering the required features (iii) A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying the services of human health (iv) the proposed system’s performance is observed in real time in multiple health databases. e results are compared with the recent healthcare monitoring systems
Summary
Rasha M Abd El-Aziz ,1 Rayan Alanazi ,1 Osama R Shahin ,1 Ahmed Elhadad ,1 Amr Abozeid ,1 Ahmed I Taloba ,1 and Riyad Alshalabi. E proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoTsensors are employed, which collect real healthcare data. Ese data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. Is algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. E proposed system’s performance is examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. e proposed system’s performance is examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring
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