Iot based patient monitoring and diagnostic prediction tool using ensemble classifier

Ani, R.; Krishna, Sooraj; N, Anju; Aslam, Masood; Deepa, O. S.

doi:10.1109/icacci.2017.8126068

Cited by 83 publications

(36 citation statements)

References 6 publications

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“…Random Forest is a supervised learning technique in which a myriad of decision trees are trained on different subsets of training set chosen randomly. Example of IoT use-case that utilized random forest is diagnosis and prediction of diseases [67].…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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“…In addition, as diabetes is a chronic disease, it is normal to find many readings for each feature in different time. These data could be collected from sensors connected to the patient body [72]. These temporal data need special analysis, which can benefit in remote patient monitoring.…”

Section: The Proposed Ensemble Evaluationmentioning

confidence: 99%

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

et al. 2019

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Early diagnosis of diabetes mellitus (DM) is critical to prevent its serious complications. An ensemble of classifiers is an effective way to enhance classification performance, which can be used to diagnose complex diseases, such as DM. This paper proposes an ensemble framework to diagnose DM by optimally employing multiple classifiers based on bagging and random subspace techniques. The proposed framework combines seven of the most suitable and heterogeneous data mining techniques, each with a separate set of suitable features. These techniques are k-nearest neighbors, naïve Bayes, decision tree, support vector machine, fuzzy decision tree, artificial neural network, and logistic regression. The framework is designed accurately by selecting, for every sub-dataset, the most suitable feature set and the most accurate classifier. It was evaluated using a real dataset collected from electronic health records of Mansura University Hospitals (Mansura, Egypt). The resulting framework achieved 90% of accuracy, 90.2% of recall = 90.2%, and 94.9% of precision. We evaluated and compared the proposed framework with many other classification algorithms. An analysis of the results indicated that the proposed ensemble framework significantly outperforms all other classifiers. It is a successful step towards constructing a personalized decision support system, which could help physicians in daily clinical practice.

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“…IoT based medical applications were carried by calling the RESTful API. In [5], Using IoT, they proposed a disease prediction and monitoring system for stroke patients. The system implemented a microcontroller connected to various wearable sensors and cloud, in which the Input values were collected using sensors and sent to the cloud for storage from where the alert messages get generated.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT based e – health care system

Chandrasekaran¹,

Farheen²,

Hemalatha³

et al. 2018

IJET

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One of the most important technological evolutions of our time is CLOUD COMPUTING, which describes the web computing power to store and process the information. The evolution and advancements are swiftly increasing in remote monitoring and Telemedicine. This paper aims at transmitting the physiological parameters of the subject to the private cloud called Thing Speak, an IOT based Sensor monitoring system. The physiological parameters are sent to the cloud via ESP8266 (IOT device). The cloud computing helps the physician to be connected with the patient’s data and it is helpful in monitoring the patients at any time through internet.

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Iot based patient monitoring and diagnostic prediction tool using ensemble classifier

Cited by 83 publications

References 6 publications

Data Analytics in the Internet of Things: A Survey

Data Analytics in the Internet of Things: A Survey

A Comprehensive Medical Decision–Support Framework Based on a Heterogeneous Ensemble Classifier for Diabetes Prediction

IOT based e – health care system

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