Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System

Norouzi, Jamshid; Yadollahpour, Ali; Mirbagheri, Seyed Ahmad; Mazdeh, M Mahdavi; Hosseini, Seyed Ahmad

doi:10.1155/2016/6080814

Cited by 81 publications

(60 citation statements)

References 15 publications

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“…Further when setting up a benchmark for our system Adaptive Neurofuzzy Inference System (ANFIS) is seen as one of the successful approach for diagnosis of chronic kidney diseases [9]. It is focused over a single disease where Multilayer perception (MLP) neural network [5] is tested over eight diseases with 97% accuracy level achieved.…”

Section: B Proposed Data Modelling Methodologymentioning

confidence: 99%

“…Considering chronic kidney disease (CKD) [9], accurate prediction with time is important for lowering cost and mortality rate. Adaptive Neurofuzzy Inference System (ANFIS) is proposed that uses real clinical data of 10 years for newly diagnosed patients with CKD to predict renal failure time frame.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Statistical model based on HUM analysis [11] over biomarkers showed 65% accurate classification. ANFIS [9] is evaluated by accessing Normalized Mean Absolute Error that is lower than 5%. PAPNET [6] is said to have diagnostic accuracy of 90%, sensitivity of 81%, and 92% specificity.…”

Section: Analysis Of Previous Diagnostics Systemsmentioning

confidence: 99%

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Context Aware SmartHealth Cloud Platform for Medical Diagnostics

Shafqat¹,

Abbasi²,

Khan³

et al. 2018

ijacsa

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Abstract-Healthcare has seen a great evolution in current era in terms of new computer technologies. Intensive medical data is generated that opens up research in healthcare analytics. Coping with this intensive data along with making it meaningful to deliver knowledge and be able to make decisions are the most important tasks. To deduce the authenticity of the data on basis of precision, correction, associations and true meaning is important to validate the understanding of correct semantics. In case of medical diagnosis to form accurate understanding of associations while removing ambiguity and forming a correct picture of the case is of utmost importance. To come up with the right metrics for the diagnostic solution we have explored the known criteria to validate healthcare analytics techniques involved in formation of diagnosis that results in betterment and safety of patients under observations and heading towards possible treatments. In this work, we have proposed a thematic taxonomy for the comparison of existing healthcare analytics techniques with emphasis on diabetes and its underlying diseases. This analysis lead us to propose a data model for hybrid distributed simulation model for future Context Aware SmartHealth cloud platform for diagnostics. This platform is designed to inherit smartness of unsupervised learning which in turn would keep updating itself under supervised learning by qualified experts. Finally, the accuracy would be determined using HUM approach with biomarkers or a better accuracy model than AUC. The recommended action plan is also presented.

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Section: B Proposed Data Modelling Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Context Aware SmartHealth Cloud Platform for Medical Diagnostics

Shafqat¹,

Abbasi²,

Khan³

et al. 2018

ijacsa

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show abstract

“…There are numerous systems available for disease prediction. Major disease domains were data mining techniques are used for prediction include heart disease [1], [2], [3], [4], kidney disease [5], [6] diabetes [7], [8] and liver disease [9], [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Predicting hyperlipidemia using enhanced ensemble classifier

Lakshmi

Vadivu

Subramanian

2018

IJET

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Advancement in medical technology has resulted in bulk creation of electronic medical health records. These health records contain valuable data which are not fully utilized. Efficient usage of data mining techniques helps in discovering potentially relevant facts from medical records. Classification plays an important role in disease prediction. In this paper we developed a prediction model for predicting hyperlipidemia based on ensemble classification. Support Vector Machine, Naïve Bayes Classifier, KNN Classifier and Decision Tree method are combined for developing the ensemble classifier. Performance of each classifier is evaluated separately. An overall accuracy of 97.07% has been obtained by using ensemble approach which is better than the performance of each classifier.

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“…It is suggested that utilizing this method, combined with the clinical tools for diagnosis of various diseases and conditions, may greatly decrease incorrect diagnoses. The fuzzy expert approach is more effective than machine-learning practices (14). Tables 1,2 depict the main expert systems that were developed for diagnosis purposes in various diseases and respiratory system diseases respectively.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy rule-based expert system for diagnosing cystic fibrosis

Hassanzad¹,

Orooji²,

Valinejadi³

et al. 2017

Electron Physician

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BackgroundFinding a valid diagnosis is mostly a prolonged process. Current advances in the sector of artificial intelligence have led to the appearance of expert systems that enrich the experiences and capabilities of doctors for making decisions for their patients.ObjectiveThe objective of this research was developing a fuzzy expert system for diagnosing Cystic Fibrosis (CF).MethodsDefining the risk factors and then, designing the fuzzy expert system for diagnosis of CF were carried out in this cross-sectional study. To evaluate the performance of the proposed system, a dataset that corresponded to 70 patients with respiratory disease who were serially admitted to the CF Clinic in the Pediatric Respiratory Diseases Center, Masih Daneshvari Hospital in Tehran, Iran during August 2016 to January 2017 was considered. Whole procedures of system construction were implemented in a MATLAB environment.ResultsResults showed that the suggested system can be used as a strong diagnostic tool with 93.02% precision, 89.29% specificity, 95.24% sensitivity and 92.86% accuracy for diagnosing CF. There was also a good relationship between the user and the system through the appealing user interface.ConclusionThe system is equipped with information, knowledge, and expertise from certified specialists; hence, as a training tool it can be useful for new physicians. It is worth mentioning that the accomplishment of this project depends on advocacy of decision making in CF diagnosis. Nevertheless, it is expected that the system will reduce the number of false positives and false negatives in unusual cases.

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Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System

Cited by 81 publications

References 15 publications

Context Aware SmartHealth Cloud Platform for Medical Diagnostics

Context Aware SmartHealth Cloud Platform for Medical Diagnostics

Predicting hyperlipidemia using enhanced ensemble classifier

A fuzzy rule-based expert system for diagnosing cystic fibrosis

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