An Effective Data Science Technique for IoT-Assisted Healthcare Monitoring System with a Rapid Adoption of Cloud Computing

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.

A Comprehensive Review of Healthcare Prediction using Data Science with Deep Learning

“…The obtained results of the suggested method correctly handle big amount of data and enhances classification accuracy and prediction of drug side effect. M Abd El-Aziz R et al (2022) [68] suggested IoT supported health monitoring system, which improves the data processing efficiency with the rapid adoption of cloud computing and expands data access in the cloud. The collected information from the real-time environment was stored in the cloud for data science processing.…”

Real Time Patient Vital Monitoring and Alarm System with Prediction of Anomalies and Future Clinical Episodes using Machine Learning Models

“…According to M Abd El-Aziz et al, [13], Data Science Technique is used for data collection and processing so that this data can visualised in real time. They also mention an Improved Pigeon Optimisation Algorithm for data col-lection, compilation, and storage for enhancing the speed of forecasting.…”

“…Machine Learning models to predict and classify if the user is susceptible to any underlying condition that may be of future concern Aldahiri et al, [1]. Cloud storage to make data available at any time M Abd El-Aziz et al, [13]. The prediction model for efficient forecasting.…”

Cloud Computing Based Network Analysis in Smart Healthcare System with Neural Network Architecture

“…By the by, it is trying to find or access clinical experts and emergency clinics in a climate of a savvy city. Frequently basic patients need a quick reaction and pressing thoughtfulness regarding saving their life [3]. Thus, information recorded from patients should be moved and deciphered with the insignificant postponement, and the outcomes should be adequately exact to be involved by clinical experts for starting assessment.…”