IoT plays an important role in the healthcare domain for improving the quality of patient care. To analyze the patients' healthcare data, a real-time health-monitoring system is required. The... Show More

IoT plays an important role in the healthcare domain for improving the quality of patient care. To analyze the patients' healthcare data, a real-time health-monitoring system is required. The proposed framework in this work is cable of such monitoring and sending alerts on critical circumstances. In this framework, the use of IoT devices makes it possible. This is very helpful in taking care of especially old wards and children in the absence or their caretakers. The function of alerting the caretakers and to inform hospital in critical condition makes this system one of its kind. Readings of patient pulse rates are taken from the pulse rate sensor and the body temperature is measured by MAX30205, a temperature sensor. The data is collected through sensors and sent over the cloud servers. Linear regression is used for further analysis and prediction of pulse and temperature trend lines. Corresponding health repots will be sent to the nearby hospitals and registered mobile numbers. The framework is validated with real-time patient data, and prediction is made regarding the trends.

The increasing adoption of transmission of medical images through internet in healthcare has led to several security threats to patient medical information. Permitting quiet data to be in peril may... Show More

The increasing adoption of transmission of medical images through internet in healthcare has led to several security threats to patient medical information. Permitting quiet data to be in peril may prompt hopeless harm, ethically and truly to the patient. Accordingly, it is important to take measures to forestall illicit access and altering of clinical pictures. This requests reception of security components to guarantee three fundamental security administrations – classification, content-based legitimacy, and trustworthiness of clinical pictures traded in telemedicine applications. Right now, inside created symmetric key cryptographic capacities are utilized. Pictorial model-based perceptual image hash is used to provide content-based authentication for malicious tampering detection and localization. The presentation of the projected algorithm has been evaluated using performance metrics such as PSNR, normalized correlation, entropy, and histogram analysis, and the simulation results show that the security services have been achieved effectively.

In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients... Show More

In the healthcare industry, sources look after different customers with diverse diseases and complications. Thus, at the source, a great amount of data in all aspects like status of the patients, behaviour of the diseases, etc. are collected, and now it becomes the job of the practitioner at source to use the available data for diagnosing the diseases accurately and then prescribe the relevant treatment. Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. The inharmonious behavior of the data is the motivation behind the development of new tools and demonstrates the available information to some meaningful information for decision making. As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed, which suits the purpose of researchers in the enhancement of their research in the domain of medical decision making.

This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed... Show More

This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.