Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques

Ayon, Safial Islam; Islam, Md. Milon; Hossain, Rahat

doi:10.1080/03772063.2020.1713916

Cited by 160 publications

(106 citation statements)

References 34 publications

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“…Many researchers have demonstrated various approaches to detect COVID-19 utilizing X-ray images. Recently, computer vision [ 10 ], machine learning [ [11] , [12] , [13] ], and deep learning [ 14 , 15 ] have been used to automatically diagnose several diverse ailments in the human body, which ensures smart healthcare [ 16 , 17 ]. The deep learning method is used as a feature extractor that enhances classification accuracies [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

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525

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Nowadays, automatic disease detection has become a crucial issue in medical science due to rapid population growth. An automatic disease detection framework assists doctors in the diagnosis of disease and provides exact, consistent, and fast results and reduces the death rate. Coronavirus (COVID-19) has become one of the most severe and acute diseases in recent times and has spread globally. Therefore, an automated detection system, as the fastest diagnostic option, should be implemented to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 4575 X-ray images, including 1525 images of COVID-19, were used as a dataset in this system. The experimental results show that our proposed system achieved an accuracy of 99.4%, AUC of 99.9%, specificity of 99.2%, sensitivity of 99.3%, and F1-score of 98.9%. The system achieved desired results on the currently available dataset, which can be further improved when more COVID-19 images become available. The proposed system can help doctors to diagnose and treat COVID-19 patients easily.

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Section: Introductionmentioning

confidence: 99%

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Islam

Asraf

2020

Informatics in Medicine Unlocked

Self Cite

525

278

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“…There are several fatal diseases like heart disease [21], diabetes [22], breast cancer [23,24], liver disorder [25], etc. in medical sector but the main concern of our developed system is to monitor the fundamental signs of all types of patients and the patient's room environment.…”

Section: Introductionmentioning

confidence: 99%

Development of Smart Healthcare Monitoring System in IoT Environment

2020

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Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great concern to many countries worldwide nowadays. The advent of Internet of Things (IoT) technologies facilitates the progress of healthcare from face-to-face consulting to telemedicine. This paper proposes a smart healthcare system in IoT environment that can monitor a patient's basic health signs as well as the room condition where the patients are now in real-time. In this system, five sensors are used to capture the data from hospital environment named heart beat sensor, body temperature sensor, room temperature sensor, CO sensor, and CO 2 sensor. The error percentage of the developed scheme is within a certain limit (< 5%) for each case. The condition of the patients is conveyed via a portal to medical staff, where they can process and analyze the current situation of the patients. The developed prototype is well suited for healthcare monitoring that is proved by the effectiveness of the system.

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“…Logistic regression (LR) is used to determine the association between categorical dependent variables against the independent variables [9]. LR is used when the dependent variable has two values such as 0 and 1, yes and no or true and false and thus it is called binary logistic regression [22]. However, when the dependent variable has more than two Let, assume the dependent values are numerical of 1 and 0 where 0 represents negative value and 1 positive value as a binary variable.…”

Section: Dataset Preparationmentioning

confidence: 99%

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery

et al. 2020

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Novel coronavirus (COVID-19 or 2019-nCoV) pandemic has neither clinically proven vaccine nor drugs; however, its patients are recovering with the aid of antibiotic medications, anti-viral drugs, and chloroquine as well as vitamin C supplementation. It is now evident that the world needs a speedy and quicker solution to contain and tackle the further spread of COVID-19 across the world with the aid of non-clinical approaches such as data mining approaches, augmented intelligence and other artificial intelligence techniques so as to mitigate the huge burden on the healthcare system while providing the best possible means for patients' diagnosis and prognosis of the 2019-nCoV pandemic effectively. In this study, data mining models were developed for the prediction of COVID-19 infected patients' recovery using epidemiological dataset of COVID-19 patients of South Korea. The decision tree, support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor algorithms were applied directly on the dataset using python programming language to develop the models. The model predicted a minimum and maximum number of days for COVID-19 patients to recover from the virus, the age group of patients who are of high risk not to recover from the COVID-19 pandemic, those who are likely to recover and those who might be likely to recover quickly from COVID-19 pandemic. The results of the present study have shown that the model developed with decision tree data mining algorithm is more efficient to predict the possibility of recovery of the infected patients from COVID-19 pandemic with the overall accuracy of 99.85% which stands to be the best model developed among the models developed with other algorithms including support vector machine, naive Bayes, logistic regression, random forest, and K-nearest neighbor.

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Coronary Artery Heart Disease Prediction: A Comparative Study of Computational Intelligence Techniques

Cited by 160 publications

References 34 publications

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images

Development of Smart Healthcare Monitoring System in IoT Environment

Predictive Data Mining Models for Novel Coronavirus (COVID-19) Infected Patients’ Recovery

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