Towards IoT and ML Driven Cardiac Status Prediction System

Zaman, Md. Ittahad Uz; Tabassum, Shaira; Ullah, Md. Sabbir; Rahaman, Ashikur; Nahar, Samsun; Islam, A. K. M. Muzahidul

doi:10.1109/icasert.2019.8934639

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…The classification of arrhythmic heart beats is accomplished through the use of linear regression. Zaman et al [11] proposed a cardiac status prediction method based on IoT and Machine Learning. The data collected from the human body were normalized before being used by machine learning algorithms to calculate and predict the overall condition of a patient's heart, the results were quite satisfactory.…”

Section: Related Workmentioning

confidence: 99%

Machine learning-based identification of patients with a cardiovascular defect

2021

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Cardiovascular diseases had been for a long time one of the essential medical problems. As indicated by the World Health Association, heart ailments are at the highest point of ten leading reasons for death. Correct and early identification is a vital step in rehabilitation and treatment. To diagnose heart defects, it would be necessary to implement a system able to predict the existence of heart diseases. In the current article, our main motivation is to develop an effective intelligent medical system based on machine learning techniques, to aid in identifying a patient’s heart condition and guide a doctor in making an accurate diagnosis of whether or not a patient has cardiovascular diseases. Using multiple data processing techniques, we address the problem of missing data as well as the problem of imbalanced data in the publicly available UCI Heart Disease dataset and the Framingham dataset. Furthermore, we use machine learning to select the most effective algorithm for predicting cardiovascular diseases. Different metrics, such as accuracy, sensitivity, F-measure, and precision, were used to test our system, demonstrating that the proposed approach significantly outperforms other models.

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Section: Related Workmentioning

confidence: 99%

Machine learning-based identification of patients with a cardiovascular defect

2021

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“…An IoT-ML method was investigated by Akash et al ( 33 ) with the goal of predicting the condition of the cardiovascular system in the human body. The algorithm model uses machine learning (ML) techniques to compute and predict the patient cardiovascular health after it has obtained essential data from the human body.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular diseases prediction by machine learning incorporation with deep learning

Subramani¹,

Varshney

Anand

et al. 2023

Front. Med.

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It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

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“…With the popularity of ML techniques in IoT applications, Mohan, S. et al [33] tried to combine a hybrid approach of two or more techniques by merging RF and Linear Method (LM) and proposed a hybrid HRFLM approach to further improve the prediction accuracy of the model. With the gradual development of the IoT, Akash, I. et al [34] investigated an approach combining IoT and ML to predict the heart status of the human body. It first collects important data from the human body, such as heart rate, ECG signal, and cholesterol, through IoT devices (sensors), and then uses an ML algorithm model in order to calculate and predict the overall condition of the patient's heart.…”

Section: Introductionmentioning

confidence: 99%

Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion

et al. 2022

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The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, CatBoost, and Multilayer Perceptron (MLP) (10 classifiers to select the optimal base learners). In order to avoid the overfitting phenomenon generated by the base learners, we use the Logistic Regression (LR) simple linear classifier as the meta learner. We validated the proposed algorithm using a fused Heart Dataset from several UCI machine learning repositories and another publicly available Heart Attack Dataset, and compared it with 10 single classifier models. The experimental results show that the proposed stacking classifier outperforms other classifiers in terms of accuracy and applicability.

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Towards IoT and ML Driven Cardiac Status Prediction System

Cited by 8 publications

References 6 publications

Machine learning-based identification of patients with a cardiovascular defect

Machine learning-based identification of patients with a cardiovascular defect

Cardiovascular diseases prediction by machine learning incorporation with deep learning

Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion

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