An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Sarmah, Simanta Shekhar

doi:10.1109/access.2020.3007561

Cited by 129 publications

(58 citation statements)

References 26 publications

(31 reference statements)

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“…Recently, the author of [88] developed a patient monitoring system for heart patients in an IoT environment where the data from the patients are analyzed using a modified Deep Learning Modified Neural Network (DLMNN). The body-worn sensors collected data from the patients and securely sent them to the cloud for further processing.…”

Section: B Heart Diseasementioning

confidence: 99%

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Mahmoud¹,

Islam

Shehata

et al. 2021

IEEE Access

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The significant increase in the number of individuals with chronic ailments (including the elderly and disabled) has dictated an urgent need for an innovative model for healthcare systems. The evolved model will be more personalized and less reliant on traditional brick-and-mortar healthcare institutions such as hospitals, nursing homes, and long-term healthcare centers. The smart healthcare system is a topic of recently growing interest and has become increasingly required due to major developments in modern technologies, especially in artificial intelligence (AI) and machine learning (ML). This paper is aimed to discuss the current state-of-the-art smart healthcare systems highlighting major areas like wearable and smartphone devices for health monitoring, machine learning for disease diagnosis, and the assistive frameworks, including social robots developed for the ambient assisted living environment. Additionally, the paper demonstrates software integration architectures that are very significant to create smart healthcare systems, integrating seamlessly the benefit of data analytics and other tools of AI. The explained developed systems focus on several facets: the contribution of each developed framework, the detailed working procedure, the performance as outcomes, and the comparative merits and limitations. The current research challenges with potential future directions are addressed to highlight the drawbacks of existing systems and the possible methods to introduce novel frameworks, respectively. This review aims at providing comprehensive insights into the recent developments of smart healthcare systems to equip experts to contribute to the field.

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Section: B Heart Diseasementioning

confidence: 99%

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Mahmoud¹,

Islam

Shehata

et al. 2021

IEEE Access

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“…The proposed model shows a better accuracy as compared to the previously developed model. Sarmah (8) proposed a model using IOT sensors attached to the body of the patient to gather real-time data and applied the Deep learning modified neural network (DLMNN) model for heart disease prediction. The prediction from the model executes in three ways: Authentication, Encryption, and Classification.…”

Section: Related Study Literature Surveymentioning

confidence: 99%

“…Monitoring health parameters using Internet-of-Things (IoT) is a forming trend for future well-being. IoT sensors are mostly used to collect real-time vital signs and monitor the health parameters of individuals (8). Collecting, processing, and analyzing vitals help predict risk early to tackle the problem (9,10).…”

Section: Introductionmentioning

confidence: 99%

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Shah

Ahirrao

Pandya

et al. 2021

Front. Public Health

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Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.

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“…The time-frequency heat map representations along with deep convolutional neural network are used to classify sound of heart automatically in [25]. IoT centered Deep Learning Modified Neural Network is presented in [27] to observe the patients with heart problem. This procedure helps in performing appropriate diagnosis and provide proper medication.…”

Section: Related Workmentioning

confidence: 99%

Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes

Shafi

Obaidat

Krishna

et al. 2021

Multimed Tools Appl

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The prediction of abnormality in the heart functionality at an early stage increases the chances of saving the life of people. Thus, this paper proposes a technique which predicts the abnormality in the functionality of the heart using heart rate in the form of beats per minute using wearable devices and deep learning model. The devices used are wrist strap and devices that can be fixed near the chest of the person or back of the person where heart beat can be detected. The proposed system is divided into 3 modules: (1) data collection and processing module, (2) prediction module and (3) communication module. First module is used to collect data and process, while prediction module predicts the abnormal functionality of the heart using deep learning model. One of the advantage of the proposed work in this paper is communication module as the communication is given to the doctor who can perform analysis before the patient reaches the hospital. A message is sent to the ambulance so that it reaches the destination on time. The message related to first aid is sent to two dear ones and the patient such that appropriate measures can be taken. The proposed technique is evaluated in terms of sensitivity, specificity, F 1 -Score, time and ROC curve metrics. It is also compared with the Two Stage Neural Network and TSNN and proved to be performing better.

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An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Cited by 129 publications

References 26 publications

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects

Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk

Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes

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