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The kidneys are very vital organs. Failing kidneys lose their ability to filter out waste products, resulting in kidney disease. To extend or save the lives of patients with impaired kidney function, kidney replacement is typically utilized, such as hemodialysis. This work uses an entropy function to identify key features related to hemodialysis. B...
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... is, the kidney that has been damaged exceeds 1/3 when HD is required [3]. Thus, indexes such as the albumin globulin ratio (A/G ratio) of kidney function (Table 1), red blood cell (RBC) count in blood tests (Table 2), or white blood cell (WBC) count by urinalysis (Table 3) are related to kidney function [1]. This work proposes an effective scheme that identifies unknown key features to predict HD. ...
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... 9 patients are nondi- alysis (Dialysis = 0), occurrence probability is P 0 = 9/15, information gain is P 0 × log(1/P 0 ) = (9/15) × log(9/15) = 0.442179, and total information gain of P 0 and P 1 is 0.970951. Age WBC RBC HB BUN CRE UA GOT GPT TP ALB GLO A/G TG Dialysis 2 5 4 3 3 4 2 2 4 5 2 2 2 2 3 2 1 1 3 1 1 1 3 1 0 0 0 0 0 0 0 1 1 2 0 1 3 1 0 0 0 0 0 1 1 0 1 0 1 1 3 0 1 1 0 0 1 0 0 1 1 0 0 0 0 1 1 4 0 2 0 1 0 0 0 0 2 0 0 0 0 0 2 0 5 1 3 1 1 1 2 1 0 3 4 1 1 1 0 1 0 6 1 1 1 1 2 1 1 1 0 0 0 0 0 0 1 1 7 0 4 2 0 0 1 1 0 0 0 1 0 1 0 1 0 8 1 1 1 1 2 3 1 0 2 4 0 0 0 1 2 1 9 1 2 0 1 2 2 1 1 1 2 0 0 0 1 1 0 10 0 2 3 1 0 2 0 1 2 1 0 0 1 0 2 0 11 1 0 1 2 2 Next, this work calculates the information gain of each item relative to dialysis item. Take Sex (Table 5) as an exam- ple. ...
Citations
... Some studies reported that ANN and other data mining methods supported medical decisions regarding VUR and some nephrological problems (21)(22)(23)(24)(25)(26)(27). ...
Aim:Vesicoureteral reflux (VUR) and urinary tract infection (UTI) are common problems in children. Our goal is to use different models for the clinical decision of differential diagnosis of VUR and UTI in children.Materials and Methods:This was a retrospective cross-sectional study with 611 pediatric patients enrolled. Detailed information for the patients was obtained from hospital records and patient files. Three models including different variables were evaluated via an artificial neural network for the differential diagnosis of VUR and recurrent UTI. Clinical findings were included in Model 1, clinical and laboratory findings were included in Model 2, and clinical, laboratory and detailed urinary ultrasonography (USG) findings were included in Model 3. A cross-validation technique was used to evaluate predictive models by partitioning the original sample into a training set to train the model, and a test set to evaluate it.Results:Of the 611 children, 425 (69.6%) had VUR and 186 (30.4%) had UTI. The sensitivity of Model 1 and Model 2 were 0.682 and 0.856, respectively. Also, Model 3 showed the best performance and highest sensitivity with 0.939 for differential diagnosis.Conclusion:Differential diagnosis between VUR and UTI in children can be predicted by using clinical, laboratory and USG variables via an Artificial Neural Network. Model 3, which included clinical, laboratory and USG variables together, showed the best performance and highest sensitivity.
... The study was single sited and the authors failed to find out infrequent occurring laboratory results, medications and problems in the dataset [4]. In 2012, Tzu-Chuen Lu et al. proposed a data mining technique which is used to determine the association rules from each cluster of hemodialysis patient data [5]. They collected the data from a hospital in Taiwan [5]. ...
... In 2012, Tzu-Chuen Lu et al. proposed a data mining technique which is used to determine the association rules from each cluster of hemodialysis patient data [5]. They collected the data from a hospital in Taiwan [5]. But they couldn't provide their complete dataset and it was very difficult to generate association rules from dataset. ...
... Three clinical parameters, Bilirubin, Creatinine and INR (International Normalized Ratio) are the constituents of MELD score [5]. The MELD score can be calculated by equation 1 [15,9]. ...
Medical databases contain large volume of data about patients and their clinical information. For extracting the features and their relationships from a huge database, various data mining techniques need to be employed. As Liver transplantation is the curative surgical procedure for the patients suffering from end stage liver disease, predicting the survival rate after Liver transplantation has a big impact. Appropriate selection of attributes and methods are necessary for the survival prediction. Liver transplantation data with 256 attributes were collected from 389 attributes of the United Nations Organ Sharing registry for the survival prediction. Initially 59 attributes were filtered manually, and then Principal Component Analysis (PCA) was applied for reducing the dimensionality of the data. After performing PCA, 197 attributes were obtained and they were ranked into 27 strong/relevant attributes. Using association rule mining techniques, the association between the selected attributes was identified and verified. Comparison of rules generated by various association rules mining algorithm before and after PCA was also carried out for affirming the results. The various rule mining algorithms used were Apriori, Treap mining and Tertius algorithms. Among these algorithms, Treap mining algorithm generated the rules with high accuracy. A Multilayer Perceptron model was built for predicting the long term survival of patients after Liver transplantation which produced high accuracy prediction result. The model performance was compared with Radial Basis Function model to prove the accuracy of survival of liver patients'. The top ranked attributes obtained from rule mining were fed to the models for effective training. This ensures that Treap mining generated associations of high impact attributes which in-turn made the survival prediction flawless.
... En [4] [9] [10], [11], [12] se aplican diversas técnicas de MD para el diagnóstico de la diabetes pero en ellos no se aborda el tema de las complicaciones asociadas como la RD, PD o ND. En [13] y [14] se reporta la aplicación de MD a la predicción de condiciones específicas de hemodiálisis (necesidad de hospitalización, diálisis) sin relacionarla directamente con variables de DMTII. En [15] se predice la probabilidad de que los pacientes con DMTII padezcan una enfermedad cardiaca. ...
... This is useful since some poor variables in this respect might be abandoned in future research if they do not provide useful information for medicine. Variable or feature evaluation and selection is an important phase in data analysis, as in, for example, [14,15], because it is necessary to find which variables most affect classification and also those which are less influential. If there are a particularly large number of variables, variable selection is important. ...
Nystagmus recordings frequently include eye blinks, noise, or other corrupted segments that, with the exception of noise, cannot be dampened by filtering. We measured the spontaneous nystagmus of 107 otoneurological patients to form a training set for machine learning-based classifiers to assess and separate valid nystagmus beats from artefacts. Video-oculography was used to record three-dimensional nystagmus signals. Firstly, a procedure was implemented to accept or reject nystagmus beats according to the limits for nystagmus variables. Secondly, an expert perused all nystagmus beats manually. Thirdly, both the machine and the manual results were united to form the third variation of the training set for the machine learning-based classification. This improved accuracy results in classification; high accuracy values of up to 89% were obtained.
In order to help individuals effectively manage and record these health check physiological measurement data, this research developed an “Integrated Health Check Report Analysis and Tracking Platform” together with H&B Health Centers. Using this platform, the public can query health check data and analysis charts from health check centers through a website. The information will include suggestions from doctors, nutritionists and representatives of various health check categories. The meanings behind various data can be explained to the general public using illustrations.
