CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Ahamed, Jameel; Koli, Abdul Manan; Ahmad, Khaleel; Jamal, Mohd. Alam; Gupta, B. B.

doi:10.9781/ijimai.2021.09.002

Cited by 23 publications

(12 citation statements)

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“…Ahamed et al [16] propose a cardiovascular disease prediction system applying fve machine-learning algorithms (random forest, decision tree, Naive based, k-nearest neighbors, and support vector machine). Teir methodology includes four tiers as follows:…”

Section: Related Workmentioning

confidence: 99%

“…Ahamed et al [ 16 ] propose a cardiovascular disease prediction system applying five machine-learning algorithms (random forest, decision tree, Naive based, k-nearest neighbors, and support vector machine). Their methodology includes four tiers as follows: Data collection: tier 1 collects data from IoT sensors or wearables devices using ThingSpeak Cloud, which is an Internet of things analytics service that allows aggregating, visualizing, and analyzing live data streams in the cloud [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

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IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

Cañón-Clavijo

Herrera‐Viedma

Gaona-García

et al. 2023

Journal of Healthcare Engineering

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Internet of Things (IoT) technologies allow building a digital representation of people, objects, or physical phenomena to be available on the Internet. Thus, stakeholders can access this information from remote places or computational systems could analyze this data to find patterns, make decisions, or execute actions. For instance, a doctor could diagnose patients by analyzing the received data from an IoT system even when patients are located in a remote place. This article proposes an IoT system for monitoring electrocardiogram (ECG) signal and processing heart data in order to generate an alert when an arrhythmia is present. This system involves a Polar H10 heart sensor, machine-learning models to classify heart events, and communication technology to share and store patient’s information. In the first place, the architecture of the IoT monitoring system and the communication between the components are described by discussing the designing criteria. Second, the experimentation process performs the training and the assessment of three classification algorithms, random forest, convolutional neural network, and k-nearest neighbors. The results show that k-nearest neighbor has the best accuracy percentage classifying the arrhythmias under study (premature ventricular contraction 94%, fusion of ventricular beat 81%, and supraventricular premature beat 82%); also, it is able to discern normal and unclassifiable beats with 93% and 97%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

Cañón-Clavijo

Herrera‐Viedma

Gaona-García

et al. 2023

Journal of Healthcare Engineering

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show abstract

“…Reference [17] proposed a novel method for using parameters to predict cardiovascular disease in India. The authors used Python-based machine learning techniques for this study: RF, DT, NB, KNN, and SVM.…”

Section: Literature Reviewmentioning

confidence: 99%

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

El-Ghany¹,

El-Aziz²

2023

Computers, Materials &Amp; Continua

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Coronary artery disease (CAD) is one of the most authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue. The breakdown of coronary cardiovascular disease is one of the principal sources of death all over the world. Cardiovascular deterioration is a challenge, especially in youthful and rural countries where there is an absence of humantrained professionals. Since heart diseases happen without apparent signs, high-level detection is desirable. This paper proposed a robust and tuned random forest model using the randomized grid search technique to predict CAD. The proposed framework increases the ability of CAD predictions by tracking down risk pointers and learning the confusing joint efforts between them. Nowadays, the healthcare industry has a lot of data but needs to gain more knowledge. Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease (HD). We evaluated the proposed framework over two public databases, Cleveland and Framingham datasets. The datasets were preprocessed by using a cleaning technique, a normalization technique, and an outlier detection technique. Secondly, the principal component analysis (PCA) algorithm was utilized to lessen the feature dimensionality of the two datasets. Finally, we used a hyperparameter tuning technique, randomized grid search, to tune a random forest (RF) machine learning (ML) model. The randomized grid search selected the best parameters and got the ideal CAD analysis. The proposed framework was evaluated and compared with traditional classifiers. Our proposed framework's accuracy, sensitivity, precision, specificity, and f1score were 100%. The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

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“…The first architecture configuration falling under the first group was presented by Ahamed et al in [ 36 ], who defined a generic architecture that combines IoT-aware wearable devices with Machine Learning and Cloud Computing techniques for the prediction of heart disease. In this architecture, the wearable devices transmit data directly to a Cloud platform containing data processing, storage, and visualisation facilities that can be accessed by the patient or medical practitioners from anywhere.…”

Section: State-of-the-artmentioning

confidence: 99%

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

Shumba

Montanaro

Sergi

et al. 2022

Sensors

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Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.

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CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Cited by 23 publications

References 0 publications

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications

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