Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

Khan, Muhammad Adnan; Abbas, Sagheer; Atta, Ayesha; Ditta, Allah; Alquhayz, Hani; Khan, Muhammad Farhan; Rahman, Atta

doi:10.32604/cmc.2020.011416

Cited by 40 publications

(10 citation statements)

References 10 publications

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“…The dataset was randomly divided into training (70% of the samples, 105,971) and validation (30% of the samples, 45,417). Various statistical parameters such as accuracy, misclassification rate (MCR), selectivity, recall, precision, false positive rate, false omission rate (FOR), false discovery rate (FDR), F 0.5 score and F 1 score are considered for investigating the performance of the proposed HSPS-WFML model [43][44][45].…”

Section: Simulations and Resultsmentioning

confidence: 99%

Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning

2022

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The hydrogen stored in liquid organic hydrogen carriers (LOHCs) has an advantage of safe and convenient hydrogen storage system. Dibenzyltoluene (DBT), due to its low flammability, liquid nature and high hydrogen storage capacity, is an efficient LOHC system. It is imperative to indicate the optimal reaction conditions to achieve the theoretical hydrogen storage density. Hence, a Hydrogen Storage Prediction System empowered with Weighted Federated Machine Learning (HSPS-WFML) is proposed in this study. The dataset were divided into three classes, i.e., low, medium and high, and the performance of the proposed HSPS-WFML was investigated. The accuracy of the medium class is higher (99.90%) than other classes. The accuracy of the low and high class is 96.50% and 96.40%, respectively. Moreover, the overall accuracy and miss rate of the proposed HSPS-WFML are 96.40% and 3.60%, respectively. Our proposed model is compared with existing studies related to hydrogen storage prediction, and its accuracy is found in agreement with these studies. Therefore, the proposed HSPS-WFML is an efficient model for hydrogen storage prediction.

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Section: Simulations and Resultsmentioning

confidence: 99%

Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning

2022

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“…For example, devices such as the Amazfit Band 1S (PPG and single-lead ECG) [ 30 ], the HealthyPiV3 biosensors [ 31 ], or Polar H7 HR monitor [ 32 ] have been utilized. A few research groups have even built their own wearable ECG recording prototypes [ 33 , 34 , 35 ].…”

Section: Cardiovascular Systemmentioning

confidence: 99%

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Neri

Oberdier

Abeelen

et al. 2023

Sensors

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Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

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“…Khan et al [16] applied various methods, namely DNNs, RF, voting, variation autoencoders, and stacking ML classifiers, for handling unfair datasets to develop a dynamic system using the most current intrusion detection dataset [17]. The efficiency of the sampling techniques was tested.…”

Section: Literature Reviewmentioning

confidence: 99%

Network Intrusion Detection Model Using Fused Machine Learning Technique

Alotaibi¹

2023

Computers, Materials &Amp; Continua

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With the progress of advanced technology in the industrial revolution encompassing the Internet of Things (IoT) and cloud computing, cyberattacks have been increasing rapidly on a large scale. The rapid expansion of IoT and networks in many forms generates massive volumes of data, which are vulnerable to security risks. As a result, cyberattacks have become a prevalent and danger to society, including its infrastructures, economy, and citizens' privacy, and pose a national security risk worldwide. Therefore, cyber security has become an increasingly important issue across all levels and sectors. Continuous progress is being made in developing more sophisticated and efficient intrusion detection and defensive methods. As the scale of complexity of the cyber-universe is increasing, advanced machine learning methods are the most appropriate solutions for predicting cyber threats. In this study, a fused machine learning-based intelligent model is proposed to detect intrusion in the early stage and thus secure networks from harmful attacks. Simulation results confirm the effectiveness of the proposed intrusion detection model, with 0.909 accuracy and a miss rate of 0.091.

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Intelligent Cloud Based Heart Disease Prediction System Empowered with Supervised Machine Learning

Cited by 40 publications

References 10 publications

Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning

Hydrogen Storage Prediction in Dibenzyltoluene as Liquid Organic Hydrogen Carrier Empowered with Weighted Federated Machine Learning

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review

Network Intrusion Detection Model Using Fused Machine Learning Technique

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