IoT-based patient monitoring system for predicting heart disease using deep learning

Ramkumar, G.; Seetha, J.; Priyadarshini, R.; Gopila, M.; Saranya, G.

doi:10.1016/j.measurement.2023.113235

Cited by 61 publications

(6 citation statements)

References 17 publications

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“…The long short-term memory (LSTM) and recurrent neural network (RNN) based smart healthcare system achieved 99.9% accuracy in forecasting heart disease. Kalman filter is used to remove noisy data and to gather missing data, lion and krill optimization techniques were combined for feature extraction process [53].…”

Section: Performance Measuresmentioning

confidence: 99%

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

Chaithra,

Siddesha,

Aradhya

et al. 2024

RIA

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Heart disease stands as a principal cause of death worldwide, and its early prediction is essential for effective patient management and the reduction of healthcare expenditures. In this context, machine learning (ML) has emerged as a transformative tool in the healthcare sector, demonstrating a profound capability to discern intricate data patterns and furnish accurate prognostic assessments. The application of ML in cardiology is instrumental for risk prediction, early detection, and the customization of treatment protocols. The current study systematically reviews the spectrum of ML approaches applied to the prediction of heart disease, spanning supervised, unsupervised, reinforcement, and transfer learning methodologies. Data from prominent repositories such as Kaggle and the UCI Machine Learning Repository were employed to evaluate the performance of various ML algorithms, with key metrics including accuracy, sensitivity, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Influential predictors, namely age, gender, cholesterol levels, blood pressure, and lifestyle factors, were integral to the development of these predictive models. Particular attention was given to the exploration of ensemble methods and deep learning frameworks, which have shown to augment prediction accuracy beyond that of traditional models. This research delineates essential risk factors associated with heart disease and underscores the significance of predictive analytics in the healthcare landscape. With a focus on heterogeneous datasets and analytical techniques, the review aims to inform public health strategies and contribute to the alleviation of healthcare burdens. The elucidated findings highlight the promise of ML, particularly through the utilization of ensemble and deep learning methods, in the precursory prediction of heart disease. Such advancements enable healthcare professionals to make more informed decisions, adopt preventative interventions, and mitigate the overall impact on healthcare systems. This exhaustive review also synthesizes the efficacy and practicality of various ML algorithms, providing a valuable compendium for future research initiatives and promoting the integration of cutting-edge technologies in the management of cardiac health.

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Section: Performance Measuresmentioning

confidence: 99%

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

Chaithra,

Siddesha,

Aradhya

et al. 2024

RIA

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“…Articles [ 12 , 13 , 14 , 21 ] use IoT sensors to capture data and then use data to predict and diagnose heart diseases with very promising results. Refs.…”

Section: State Of the Artmentioning

confidence: 99%

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Elvas,

Nunes,

Ferreira

et al. 2023

JPM

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Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

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“…This event, held in Germany, marked the inception of the fourth industrial revolution, which is currently in its early stages of development [ 1 ]. Industry 4.0 incorporates two primary dimensions of advancement: the Cyber–Physical System (CPS), a comprehensive control system that integrates networks, computational capabilities, sensors, and physical objects, and the smart factory, which leverages the CPS system and the Internet of Things (IoT) to enhance manufacturing production, intelligent machinery [ 2 ], logistics supply chains [ 3 ], human–computer interaction [ 4 ], and automated control within factories [ 5 ]. The differences between the existing industries and the 4.0 model can be categorized into 3 primary domains: components that possess self-awareness and self-predictive capabilities; machines that exhibit self-awareness, self-predictive abilities, and the ability to self-compare; and a productive system that can self-configure, self-maintain, and self-organize [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

Irfan,

Liao,

Taipabu

et al. 2024

Sensors

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The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment’s operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R2) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

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IoT-based patient monitoring system for predicting heart disease using deep learning

Cited by 61 publications

References 17 publications

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

A Review of Machine Learning Techniques Used in the Prediction of Heart Disease

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

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