Diabetes Prediction: A Deep Learning Approach

Ayon, Safial Islam; Islam, Md. Milon

doi:10.5815/ijieeb.2019.02.03

Cited by 131 publications

(88 citation statements)

References 20 publications

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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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Section: Introductionmentioning

confidence: 99%

Development of Smart Healthcare Monitoring System in IoT Environment

2020

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“…Python programming language was used for data mining predictive tasks. Python is a well-known general purpose and dynamic programming language that is being used for different fields such as data mining [30], machine learning [31,32], and internet of things [33,34]. Data mining algorithms are being implemented using python with the help of the special purpose libraries.…”

Section: Resultsmentioning

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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“…Python programming language was used for data mining predictive tasks. Python is a well-known general purpose and dynamic programming language that is being used for different elds such as data mining [30], machine learning [31], [32], and internet of things [33], [34]. Data mining algorithms are being implemented using python with the help of the special purpose libraries.…”

Section: Resultsmentioning

confidence: 99%

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

Muhammad

Islam

Usman

et al. 2020

Preprint

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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 antibiotics 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.

show abstract

Diabetes Prediction: A Deep Learning Approach

Cited by 131 publications

References 20 publications

Development of Smart Healthcare Monitoring System in IoT Environment

Development of Smart Healthcare Monitoring System in IoT Environment

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

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

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