Real-time monitoring system for early prediction of heart disease using Internet of Things

An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT

The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML's area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network's lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT.Communicated by Ramaswamy H. Sarma.

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

Wireless Body Area Network (WBAN) is a trending technology of Wireless Sensor Networks (WSN) to enhance the healthcare system. This system is developed to monitor individuals by observing their physical signals to offer physical activity status as a wearable low-cost system that is considered an unremarkable solution for continuous monitoring of cardiovascular health. Various studies have discussed the uses of WBAN in Personal Health Monitoring systems (PHM) based on real-world health monitoring models. The major goal of WBAN is to offer early and fast analysis of the individuals but it is not able to attain its potential by utilizing conventional expert systems and data mining. Multiple kinds of research are performed in WBAN based on routing, security, energy efficiency, etc. This paper suggests a new heart disease prediction under WBAN. Initially, the standard patient data regarding heart diseases are gathered from benchmark datasets using WBAN. Then, the channel selections for data transmission are carried out through the Improved Dingo Optimizer (IDOX) algorithm using a multi-objective function. Through the selected channel, the data are transmitted for the deep feature extraction process using One Dimensional-Convolutional Neural Networks (ID-CNN) and Autoencoder. Then, the optimal feature selections are done through the IDOX algorithm for getting more suitable features. Finally, the IDOX-based heart disease prediction is done by Modified Bidirectional Long Short-Term Memory (M-BiLSTM), where the hyperparameters of BiLSTM are tuned using the IDOX algorithm. Thus, the empirical outcomes of the given offered method show that it accurately categorizes a patient's health status founded on abnormal vital signs that is useful for providing the proper medical care to the patients.

Heart disease prediction using IoT based framework and improved deep learning approach: Medical application

Heart disease is the biggest cause of death in the globe. The method of predicting cardiac disease is exceedingly complex. It can only be done properly if the doctor has a lot of expertise and is well-versed in the condition. IoT-based illness prediction is a relatively recent technology for accurately classifying diseases based on sensor data. This system proposes an enhanced deep learning-based framework for predicting the heart disease. The general publicly available Hungarian heart disease dataset is utilized for the implementation, which includes heart disease related data collected from patients through IoT sensor devices. The input dataset is preprocessed using Median Studentized Residual approach for resolving error data and missing values. Preprocessed data values are feature selected by Harris Hawk Optimization (HHO) approach. The selected features are then classified into normal and abnormal by Modified Deep Long Short-Term Memory (MDLSTM). The modification in LSTM output is altered using Improved Spotted Hyena Optimization (ISHO) algorithm. The results are implemented in the working platform of Phyton with the metrics such as specificity, sensitivity, F-Score, Kappa value, Accuracy, BER and Execution time. The analyzed results shows that the implemented methodology is superior in the prediction of heart disease with an accuracy of 98.01% and reduced error rate of 91.11 compared with other existing techniques.

The Impact of Healthcare 4.0 on the Healthcare Service Quality: A Systematic Literature Review

Healthcare 4.0 is inspired by Industry 4.0 and its application has resulted in a paradigmatic shift in the field of healthcare. However, the impact of this digital revolution in the healthcare system on healthcare service quality is not known. The purpose of this study is to examine the impact of healthcare 4.0 on healthcare service quality. This study used the systematic literature review methodology suggested by Transfield et al. to critically examine 67 articles. The impact of healthcare 4.0 is analyzed in-depth in terms of the interpersonal, technical, environmental, and administrative aspect of healthcare service quality. This study will be useful to hospitals and other stakeholders to understand the impact of healthcare 4.0 on the service quality of health systems. Besides, this study critically analyses the existing literature and identifies research areas in this field and hence will be beneficial to researchers. Though there are few literature reviews in healthcare 4.0, this is the first study to examine the impact of Healthcare 4.0 on healthcare service quality.

Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.