Neural Network For The Estimation Of Ammonia Concentration In Breath Of Kidney Dialysis Patients (original) (raw)
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IEEE Journal of Translational Engineering in Health and Medicine
Diabetes mellitus and its complication such as heart disease, stroke, kidney failure, etc. is a serious concern all over the world. Hence, monitoring some important blood parameters non-invasively is of utmost importance, that too with high accuracy. This paper presents an in-house developed system, which will be helpful for diabetes patients with Chronic Kidney Disease (CKD) to monitor blood urea and glucose. This manuscript discusses a comparative study for the prediction of blood urea and glucose using Backpropagation Artificial Neural Network (BP-ANN) and Partial Least Square Regression (PLSR) model. The NVIDIA Jetson Nano board controls the five fixed LED wavelengths in the Near Infrared (NIR) region from 2.0 µm to 2.5 µm with a constant emission power of 1.2 mW. The spectra for 57 laboratory prepared samples conforming with major blood constituents of the blood sample were recorded. From these samples, 53 spectra were used for training/calibration of the BP-ANN/PLSR model and the remaining 4 samples were used for validating the model. The PLSR model predicts blood urea and glucose with a Root Mean Square Error (RMSE) of 0.88 & 12.01 mg/dL, Coefficient of Determination R 2 = 0.93 & R 2 = 0.97, Accuracy of 94.2 % and 90.14 %, respectively. To improve the prediction accuracy, BP-ANN model is applied. Later the Principal Component Analysis (PCA) technique was applied to these 57 spectra values. These PCA values were used to train and validate the BP-ANN model. After applying the BP-ANN model, the prediction of blood urea & glucose improved remarkably, which achieved RMSE of 0.69 mg/dL, R 2 = 0.96, Accuracy of 95.96 % for urea and RMSE of 2.06 mg/dL, R 2 = 0.99, and Accuracy of 98.65 % for glucose. The system performance is then evaluated with Bland Altman analysis and Clarke Error Grid Analysis (CEGA). INDEX TERMS Artificial Neural Network, chronic kidney, Diabetes mellitus, diabetes nephropathy, Jetson Nano, PLSR. Clinical and Translational Impact Statement: The system designed with Machine learning accurately estimates the Blood Urea and glucose Blood concentration in the samples prepared which conforming to major constituents of human blood tissue. With these encouraging results, the device can be used directly on human cartilage tissue after ethical clearance. This device will immensely help the Diabetes mellitus patient suffering from CKD.
Air Quality, Atmosphere & Health
This work aims to obtain an artificial neural network to simulate hospitalizations for respiratory diseases influenced by pollutant gaseous such as CO, PM 10 , PM 2.5 , NO 2 , O 3 , and SO 2 emitted from 2011 to 2017, in the city of São Paulo. The hospitalization costs were also be calculated. MLP and RBF neural networks have been tested by varying the number of neurons in the hidden layer and the type of equation of the output function. The following pollutants and its concentration range were collected considering the supervision of Alto Tiete station set, in several neighborhoods in the city of São Paulo, from in the period 2011 to 2017: 28-63 µg/m 3 of PM 2.5 , 52-110 µg/m 3 of PM 10 , 49-135 µg/m 3 of O 3 , 0.8-2.6 ppm CO, 41-98 µg/m 3 of NO 2 , and 3-16 µg/m 3 of SO 2. Results showed that a RBF neural network with 6 input neurons, 13 hidden layer neurons, and 1 output neuron, using BFGS algorithm and a Gaussian function to neuronal activation, was the best fitted to the experimental datasets. So, knowing the monthly concentration of gaseous pollutions was possible to predict the hospitalization of 1464 to 3483 ± 510 patients, with costs between 570,447 and 1,357,151 ± 198,171 USD per month. This way, it is possible to use this neural network to predict the costs of hospitalizing patients for respiratory diseases and to contribute to the decision-making of how much the government should spend on health care.
Prediction of Toxic Gases Using Intelligent Multi-sensors Combined with Artificial Neural Networks
IOSR journal of engineering, 2014
The security of monitor indoor air quality using sensors is not yet widespread. However, it is an efficient way to control the toxic gazes coming from large industrial facilities when traditional instrument are not usable especially in low concentration. This paper presents the prediction's power of toxic gases using neural networks MLP off-line type. Back propagation algorithm was used to train a multi-layer feed-forward network (descent gradient algorithm).The data used in this work are stemming from a system of intelligent multi-sensors analysis and signal processing in order to detect hydrogen sulfide(H 2 S), NO 2 (nitrogen dioxide) and their mixture (H2S-NO 2) in low concentration (one ppm).The successful results based on different accuracy in terms of statistical criteria, approve the robustness of our developed model that gives a certain power for electronic nose prediction .
Developing an Intelligent System for Diagnosis of Asthma Based on Artificial Neural Network
Acta Informatica Medica, 2015
Introduction: Lack of proper diagnosis and inadequate treatment of asthma, leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different modes was made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. So considering the data mining approaches due to the nature of medical data is necessary.
Application of Neural Networks in the Medical Field
Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications
The field of computer technology has seen remarkable advancements, which has led to a surge in interest in the possible applications of "Artificial Intelligence," or AI, in the fields of medicine and biological research. The field of artificial intelligence known as "Artificial Neural Networks" (ANNs) is one of the most promising and intensively researched subfields in AI. ANNs are, in their most fundamental form, the mathematical algorithms that are generated by computers. ANNs are taught using standard data and are able to comprehend the information that is imparted by the data. ANNs that have been trained come extremely close, on a basic level, to replicating the functioning of small biological neural clusters. They are the digital model of the biological brain, and they have the ability to discover complicated nonlinear correlations between dependent and independent variables in a data set, something that the human brain may be unable to do. These days, ANNs are employed extensively for medical applications in a variety of subspecialties within the field of medicine, particularly cardiology. Diagnostics, electronic signal analysis, medical image analysis, and radiology are just few of the fields that have found considerable use for ANNs. Many researchers have made use of ANNs for modelling purposes in the field of medicine and clinical research. Both pharmacoepidemiology and medical data mining are experiencing increasingly more applications of artificial neural networks (ANNs). The author of this paper provides an overview of the many different applications of ANNs in the field of medical science.
Artificial Neural Networks in Medical Diagnosis
Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; data is the disease symptoms. The second is the heart disease; data is on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: infected and non-infected. Classification is an important tool in medical diagnosis decision support. Feed-forward back propagation neural network is used as a classifier to distinguish between infected or non-infected person in both cases. The results of applying the artificial neural networks methodology to acute nephritis diagnosis based upon selected symptoms show abilities of the network to learn the patterns corresponding to symptoms of the person. In this study, the data were obtained from UCI machine learning repository in order to diagnosed diseases. The data is separated into inputs and targets. The targets for the neural network will be identified with 1's as infected and will be identified with 0's as non-infected. In the diagnosis of acute nephritis disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 99 percent while in the diagnosis of heart disease; the percent correctly classified in the simulation sample by the feed-forward back propagation network is 95 percent.
Using Two-Layered Feed-Forward Neural Networks to Model Blood Uric Acid Among Diabetic Patients
2021
Introduction: The end product of purine metabolism in humans is uric acid (UA). Although uric acid can function as either an antioxidant or an oxidant depending on the surrounding environment, the chemical environment can also impact uric acid.The uric acid level in the serum can predict the development of diabetic nephropathy in type 1 diabetes. Objective: This study aims to determine factors that are perhaps having an association with acid uric. Method: Variables selection is basedon clinical importance. The most significant variable will be assigned and analyzed using Artificial Neural Network (ANN) through multilayer feed-forward and contour plot. Results: Through the architecture of MLFF with two hidden layers, it was found that Creatinine level, Urea level, Systolic Blood Pressure reading, Waist circumference reading, Gender play an essential role toward uric level with an accuracy of 97.7% and the predicted mean squared error (MSE.net) is 0.005. The combination of the selecte...
Neural Networks Towards Medical Diagnosis
ijmer.com
The Neural Networks are best at identifying patterns or trends in data and they are well suited for predicting or forecasting. Hence neural networks are extensively applied to biomedical systems. An analysis is carried out to motivate neural network applications ...
Detection of respiratory abnormalities using artificial neural networks
2008
Problem Statement: Lung disease is a major threat to the human health regarding the industrial life, air pollution, smoking, and infections. Lung function tests are often performed using spirometry. Approach: The present study aims at detecting obstructive and restrictive pulmonary abnormalities. Lung function tests are often performed using spirometry. In this study, the data were obtained from 250 volunteers with standard recording protocol in order to detect and classify pulmonary diseases into normal, obstructive and restrictive. Firstly, spirometric data was statistically analyzed concerning its significance for neural networks. Then, such parameters were presented as input to MLP and recurrent networks. Results: These two networks detected normal and abnormal disorders as well as obstructive and restrictive patterns, respectively. Moreover, the output data was confirmed by measuring accuracy and sensitivity. Conclusion: The results show that the proposed method could be useful for detecting the function of respiratory system.