Smart wearable model for predicting heart disease using machine learning

Rani, S.V. Jansi; Chandran, Karthik; Ranganathan, Akshaya; Chandrasekharan, M.; Janani, B.; Deepsheka, G.

doi:10.1007/s12652-022-03823-y

Cited by 14 publications

(4 citation statements)

References 35 publications

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“…In contrast, the authors in [ 114 ] used the DS18B20 temperature sensor and ADXL1335 accelerometer to develop the desired wearable system. In addition, the authors in [ 52 , 57 , 65 , 67 , 69 , 71 , 72 , 74 , 75 , 77 , 79 , 81 , 89 , 94 , 100 , 101 , 113 , 122 ] used the AD8232 ECG sensor to collect vital signs data. In these studies, as discussed in Table 2 , different processing units, connector modules, and power sources were used to build the wearable device.…”

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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“…4. S. K. Singh et al ( 2021) published "Predicting cardiac disease using ensemble learning: a comparative study" [4] in the Journal of Ambient Intelligence and Humanized Computing. Using the Cleveland dataset, the authors evaluated how well different ensemble learning techniques such as bagging, boosting, and stacking performed in predicting cardiac disease.…”

Section: Survey Of Other Workmentioning

confidence: 99%

Machine Learning-Based Cardiac Disease Prediction Survey Paper

2023

IRJMETS

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An overview of recent studies employing machine learning approaches to predict cardiac disease is given in this survey paper. One of the main causes of death in the world is cardiac disease, and effective treatment and prevention depend on early detection. Machine learning algorithms have been routinely used to evaluate sizable datasets, pinpoint probable cardiac disease risk factors, and forecast future cardiac disease propensity.In this research, we discuss different machine learning methods, including decision trees, support vector machines, artificial neural networks, and random forests, that are used to predict cardiac disease. In order to enhance the performance of these models, several feature selection and dimensionality reduction strategies are also examined.Finally, we outline potential future research avenues in this field and examine the difficulties and restrictions of utilizing machine learning to predict cardiac disease.

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“…Monitoring is done for changes in the ECG patterns. The R-to-R approach is applied to ECG patterns to determine the heart rate [29]. Artificial intelligence has also drawn greater interest recently, since it enables a deeper understanding of healthcare data and produces precise prediction outcomes.…”

Section: Related Workmentioning

confidence: 99%

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

et al. 2022

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Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.

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Smart wearable model for predicting heart disease using machine learning

Cited by 14 publications

References 35 publications

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Machine Learning-Based Cardiac Disease Prediction Survey Paper

Modified Self-Adaptive Bayesian Algorithm for Smart Heart Disease Prediction in IoT System

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