Clinical decision support system for diabetes disease diagnosis using optimized neural network

Kumar, Manoj; Sharma, Anubha; Agarwal, Sonali

doi:10.1109/sces.2014.6880051

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…A further consideration highlighting the difficulties in using novel digital technologies in the healthcare domain is that ethical considerations are not limited to security and privacy problems, even if they are evidently rel- 5 The recall denotes the fraction of correctly recognized scenarios over the number of critical scenarios to be recognized: TP/(TP+FN). 6 The precision denotes the fraction of scenarios correctly recognized as critical with respect to the total number of scenarios recognized as critical (correctly and not correctly): TP/(TP+FP). 7 Accuracy = (TP+TN)/(TP+TN+FP+FN).…”

Section: Discussionmentioning

confidence: 99%

Secure IoT Analytics for Fast Deterioration Detection in Emergency Rooms

et al. 2020

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IoT data analytics can potentially bring benefits to several critical application domains, especially in healthcare. In fact, especially in emergency rooms the detection of critical patients can be a critical task when the number of patients to be monitored is high with respect to the available medical personnel. However, it is also necessary to pay attention to ethics, privacy, and security issues, aiming to prevent attacks and unauthorized access to sensitive data of patients, guaranteeing the correct functioning of the system in a secure environment. To this end, this paper presents a knowledge representation framework enabling the intelligent video surveillance of patients, which can be used in combination with IoT-based systems to enhance the detection of critical patients in emergency rooms, while dealing with ethics, privacy, and security issues. These are guaranteed by means of an event-based visual access control specification method, constraining the access to both devices and users. We also describe a clinical scenario related to the early treatment of sepsis in an emergency room, showing how the proposed framework can enhance the detection of such critical disease while guaranteeing ethics, privacy, and security.

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Section: Discussionmentioning

confidence: 99%

Secure IoT Analytics for Fast Deterioration Detection in Emergency Rooms

et al. 2020

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“…Other applications focused on diagnostic systems for Heart Disease prediction for Coronary diseases using machine learning approaches. The machine learning methods used in these applications ranged between using a single machine learning technique such as hidden Naïve Bayes (NB), SVM, optimized ANN and Decision Tree (DT) classifiers [4,5,6,7], to using a collective or hybrid machine learning techniques [8,9,10]. Since the focus in this proposed research is on heart disease diagnosis, more attention will be devoted to its related literature.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, when speed is needed during the analysis of big data sets, the NB classifier could be an appropriate choice. The Naive Bayes classification problem could be solved by estimating a classification ratio C, see equation (6). If C is greater than 1, then the first class is predicted, if not, then predict the second class.…”

Section: The Nb Modelmentioning

confidence: 99%

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Meshref¹

2019

IJACSA

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Research on heart diseases has always been the center of attention of the world health organization. More than 17.9 million people died from it in 2016, which represent 31% of the overall deaths globally. Machine learning techniques have been used extensively in that area to assist physicians to develop a firm opinion about the conditions of their heart disease patients. Some of the existing machine learning models still suffers from limited predication ability, and the chosen analysis approaches are not suitable. As well, it was noticed that the existing approaches pay more attention to building high accuracy models, while overlooking the ability to interpret and understand the recommendations of these models. In this research, different renowned machine learning techniques: Artificial Neural Networks, Support Vector Machines, Naïve Bayes, Decision Trees and Random Forests have been investigated to help in building, understanding and interpreting different heart disease diagnosing models. The Artificial Neural Networks model showed the best accuracy of 84.25% compared to the other models. In addition, it was found that despite some designed models have higher accuracies than others, it may be safer to choose a lower accuracy model as a final design of this study. This sacrifice was essential to make sure that a more transparent and trusted model is being used in the heart disease diagnosis process. This transparency validation was conducted using a newly suggested metric: the Feature Ranking Cost index. The use of that index showed promising results by making it clear as which machine learning model has a balance between accuracy and transparency. It is expected that following the detailed analyses and the use of this research findings will be useful to the machine learning community as it could be the basis for post-hoc prediction model interpretation of different clinical data sets.

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“…The objective of data mining is to find the relevant and useful information from data and to present it to the users in presentable format or understandable format [2].…”

Section: Introductionmentioning

confidence: 99%

“…Health care is a burning issue in any developing nation and a way to express the development of any country [2]. Artificial…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction

Saji

Balachandran

2015

2015 International Conference on Advances in Computer Engineering and Applications

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Artificial Intelligence plays a vital role in developing machines or software that can create intelligence. Artificial Neural Networks is a field of neuroscience which contributes tremendous developments in Artificial Intelligence. This paper focuses on the study of performance of various training algorithms of Multilayer Perceptrons in Diabetes Prediction. In this study, we have used Pima Indian Diabetes data set from UCI Machine Learning Repository as input dataset. The system is implemented in MatlabR2013. The Pima Indian Diabetes dataset consists of about 768 instances. The input data is the patient history and the target output is the prediction result as tested positive or tested negative. From the performance analysis, it was observed that out of all the training algorithms, Levenberg-Marquardt Algorithm has given optimal training results.

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Clinical decision support system for diabetes disease diagnosis using optimized neural network

Cited by 11 publications

References 7 publications

Secure IoT Analytics for Fast Deterioration Detection in Emergency Rooms

Secure IoT Analytics for Fast Deterioration Detection in Emergency Rooms

Cardiovascular Disease Diagnosis: A Machine Learning Interpretation Approach

Performance analysis of training algorithms of multilayer perceptrons in diabetes prediction

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