Neural networks to estimate the risk for preeclampsia occurrence

Neocleous, Costas; Anastasopoulos, Panagiotis Ch.; Nikolaides, K.; Schizas, Christos N.; Kleanthous, Kleanthis

doi:10.1109/ijcnn.2009.5178820

Cited by 24 publications

(8 citation statements)

References 20 publications

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“…Akter and Uddin (2009) ( 14 ), and Karabatak and Cevdet (2009) entered the data of patients’ medical records into an expert system to evaluate the proposed system and reported 95.6% accuracy ( 15 ). Nucleous and colleagues (2009) reported sensitivity of 100% and specificity of 50% in their system assessment ( 16 ), which is lower (96.5%) than that reported in the current study.…”

Section: Discussioncontrasting

confidence: 77%

The Diagnostic Value of Skin Disease Diagnosis Expert System

et al. 2016

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Background:Evaluation is a necessary measure to ensure the effectiveness and efficiency of all systems, including expert systems. The aim of this study was to determine the diagnostic value of expert system for diagnosis of complex skin diseases.Methods:A case-control study was conducted in 2015 to determine the diagnostic value of an expert system. The study population included patients who were referred to Razi Specialized Hospital, affiliated to Tehran University of Medical Sciences. The control group was selected from patients without the selected skin diseases. Data collection tool was a checklist of clinical signs of diseases including pemphigus vulgaris, lichen planus, basal cell carcinoma, melanoma, and scabies. The sample size formula estimated 400 patients with skin diseases selected by experts and 200 patients without the selected skin diseases. Patient selection was undertaken with randomized stratified sampling and their sign and symptoms were logged into the system. Physician’s diagnosis was determined as the gold standard and was compared with the diagnosis of expert system by SPSS software version 16 and STATA. Kappa statistics, indicators of sensitivity, specificity, accuracy and confidence intervals were calculated for each disease. An accuracy of 90% was considered appropriate.Results:Comparing the results of expert system and physician’s diagnosis at the evaluation stage showed an accuracy of 97.1%, sensitivity of 97.5% and specificity of 96.5% The Kappa test indicated a high agreement of 93.6%.Conclusion:The expert system can diagnose complex skin diseases. Development of such systems is recommended to identify all skin diseases.

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Section: Discussioncontrasting

confidence: 77%

The Diagnostic Value of Skin Disease Diagnosis Expert System

et al. 2016

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“…Another study had extracted rules from the decision tree by designing an expert system that was used to diagnose neurological diseases ( 10 ). Other studies had extracted rules by a two step decision tree of training and testing ( 11 - 16 ) or had used the decision tree to extract the rules that were used to design neural networks and estimate the risk of preeclampsia with an accuracy of 83.6% in the training phase and 93.8% in the testing phase ( 17 ). The methods used in these studies are in line with the methodology used in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have used neural network in the phase of knowledge representation ( 17 ) that is not in consistence with the current study. The major problem of neural networks is that the user cannot understand the method of inference and conclusion of the system, learning system is dependent on the collection system of train data and the amount as well as the number of training courses.…”

Section: Discussionmentioning

confidence: 99%

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm

2017

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Introduction:Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5’s Algorithm.Method:An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5’s Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then.Results:The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase.Conclusion:Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

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“…Neural networks in par ticular have proved to be quite effective and also have re sulted in some relevant patents [4]- [8].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks for non-invasive chromosomal abnormality screening of fetuses

Neocleous

Nicolaïdes

Kleanthous

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Ahstract-A large number of different neural network struc tures have been constructed, trained and tested to a large data base of pregnant women characteristics, aiming at generating a classifier-predictor for the presence of chromosomal abnor malities in fetuses, namely the Trisomy 21 (Down syndrome), Trisomy 18 (Edwards syndrome), Trisomy 13 (Patau syn drome) and the Turner syndrome. The database was composed of 31611 cases of pregnant women. 31135 women did not show any chromosomal abnor malities, while the remaining 476 were confirmed as having a chromosomal anomaly ofT21, T18, T13, or Turner Syndrome. From the total of 31611 cases, 8191 were kept as a totally unknown database that was only used for the verification of the predictability of the network. In this set, 7 were of the Turner syndrome, 14 of the Patau syndrome, 42 of the Edwards syn drome and 71 of the Down syndrome.For each subject, 10 parameters were considered to be the most influential at characterizing the risk of occurrence of these types of chromosomal anomalies.The best results were obtained when using a multi-layer neu ral structure having an input, an output and three hidden lay ers. For the case of the totally unknown verification set of the 8191 cases, 98.1 % were correctly identified. The percentage of abnormal cases correctly predicted was 85.1 %. The unknown T21 cases were predicted by 78.9%, the T18 by 76.2%, the T13 by 0.0% and the Turner syndrome by 42.9%.

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Neural networks to estimate the risk for preeclampsia occurrence

Cited by 24 publications

References 20 publications

The Diagnostic Value of Skin Disease Diagnosis Expert System

The Diagnostic Value of Skin Disease Diagnosis Expert System

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm

Artificial neural networks for non-invasive chromosomal abnormality screening of fetuses

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