scholarly journals Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Anson Chui Yan Tang ◽  
Joanne Wai Yee Chung ◽  
Thomas Kwok Shing Wong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Essential Hypertension ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Diagnostic Model ◽  
Criterion Validation ◽  
Specificity And Sensitivity ◽  
Group A ◽  
Artificial Neural

In view of lacking a quantifiable traditional Chinese medicine (TCM) pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group). A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength) on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

A Back Propagation Artificial Neural Network Applied to Body Constitution in Traditional Chinese Medicine Modeling

2013 ◽  
Vol 659 ◽  
pp. 123-127
Author(s):  
Zhi Biao Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Network Architecture ◽  
Back Propagation ◽  
Good Prediction ◽  
Body Constitution ◽  
Yin Yang ◽  
Artificial Neural

In this paper, artificial neural network architecture is introduced to predict the Yin-Yang index of body constitution in traditional Chinese medicine (BCTCM). With pre-processing the inputting data by the median, the collected data is more consistent with the exact value of the characteristic parameters of BCTCM. Quasi-Newton algorithm is used to train the network model to accelerate the convergence speed of network training. Experiments show that, the result showed that they had good prediction accuracies for BCTTCM. The mean absolute error for 10 true measured points was 0.034. Therefore, the prediction model of BCTCM Yin-Yang index with BP neural network is doable.

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

scholarly journals Grade Diagnosis of Human Glioma Based on Fingerprint and Artificial Neural Network

2021 ◽  
Author(s):  
Wenyu Peng ◽  
Shuo Chen ◽  
Dongsheng Kong ◽  
Xiaojie Zhou ◽  
Xiaoyun Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Pathological Diagnosis ◽  
World Health ◽  
Low Grade ◽  
Human Glioma ◽  
Who Grade ◽  
Specificity And Sensitivity ◽  
Artificial Neural

Abstract BackgroundThe World Health Organization (WHO) grade diagnosis of cancer is essential for surgical outcomes and patient treatment. Traditional pathological grading diagnosis depends on dyes or other histological approaches, which are time-consuming (usually 1-2 days), resource-wasting, and labor-intensive. Fourier transform infrared (FTIR) spectroscopy is a rapid and nondestructive technique that has been widely used for detecting the molecular component changes, which relies on the resonant frequencies absorbance of the molecular bonds.MethodsTo overcome the disadvantages of traditional pathological diagnosis, this paper proposed a novel diagnostic method based on FTIR and artificial neural network (ANN). Firstly, the spectra of high- and low-grade human glioma that without dye were collected by FTIR spectrometer, then the raw data preprocessed with baseline correction and amide I (1649 cm-1) normalization before input into the input-layer of the ANN, after the nonlinear conversion of the neurons in the hidden-layers, the categories were presented in the output-layer. Corresponding to the decrease of the loss function, the weights of the net updated continuously, and finally, the optimized model has the power of prediction for new samples. ResultsAfter training on 6225 spectra sourced from 77 glioma patients, the ANN model reached the prediction accuracy, specificity and sensitivity evaluation metrics above 99%, which was much superior to the common classification method of principal component analysis-linear discriminate analysis (PCA-LDA) (the prediction accuracy, specificity and sensitivity are only 87%, 89% and 86%, respectively). Moreover, rather than the lipid range of 2800-3000 cm-1, the ANN learned the fingerprint characteristics of the infrared spectrum to classify the major histopathologic classes of human glioma. Especially, the diagnosis process of the novel method only requires several minutes. Compared to the traditional pathological diagnosis, the efficiency raises almost 500 times.ConclusionsThe infrared range of fingerprint is the major indicator for cancer progression, and the ANN-based diagnosis method can be streamlined, and create a complementary pathway that is independent of the traditional pathology laboratory.

scholarly journals Diagnosis Model of Volleyball Skills and Tactics Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Jiang ◽  
Kai Zhao ◽  
Xinlong Jin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Evaluation Model ◽  
Middle Layer ◽  
Diagnostic Model ◽  
Bp Network ◽  
Node Number ◽  
Sports Industry ◽  
Artificial Neural ◽  
Repeated Training

With the development of China’s sports industry, the technical and tactical level of the team is required to be higher and higher. This study mainly discusses the diagnostic model of volleyball technique and tactics based on artificial neural network. With the help of the correlation function in Matlab neural network toolbox, in the training process of volleyball technical and tactical evaluation neural network, the sample data of volleyball technical evaluation index is repeatedly simulated and studied, and finally the network parameters with the minimum error and the highest accuracy are saved as the network model for subsequent verification and evaluation. The middle layer is the hidden layer, which makes the network approach the result of volleyball experts’ evaluation of the technology by adjusting the weights of neurons. The last layer is the output layer, which outputs the actual evaluation results of volleyball experts on the technology. Through repeated training and comparison of input samples, the maximum number of training times of BP network for volleyball technical and tactical evaluation is determined to be 32. Some common experience of estimating hidden node number is provided by trial-and-error method. On this basis, the number of hidden nodes to minimize the network error is finally determined to be 4 through repeated training and comparison. In the process of network diagnosis, the average difference between the evaluation score of network output and the score of actual experts is less than 1%, which reaches a very high precision. It shows that the volleyball skill evaluation model based on BP neural network is feasible in technology and the result is relatively reliable.

scholarly journals Classification of Erythemato-squamous diseases using Artificial Neural Network and Genetic algorithm

2020 ◽  
pp. 208-212
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Experimental Result ◽  
Mining System ◽  
Statistical Measures ◽  
Specificity And Sensitivity ◽  
Artificial Neural

This paper introduces a hybrid model using artificial neural network (ANN) and genetic algorithm (GA) to develop an efficient classification technique for classification of different categories of Erythemato-squamous diseases. Neural network has been extensively used in many applications like classification, regression, web mining, system identification and pattern recognition. Weight optimization in neural network has been a matter of concern for researchers in the field of soft computing. In this paper the weights of ANN are optimized with GA. The proposed hybrid model is applied on the Erythemato-squamous dataset taken from UCI machine learning repository. The dataset contains six different categories: psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris of Erythemato-squamous diseases. The main aim of this paper is to determine the type of Eryhemato-Squamous disease using the hybrid model. The performance of the hybrid model is evaluated using statistical measures like accuracy, specificity and sensitivity. The accuracy of the proposed model is found to be 99.34% on test dataset. The experimental result shows the effectiveness of the hybrid model in classification of Erythematosquamous diseases.

scholarly journals Applications of Soft Computing Techniques for Pulmonary Tuberculosis Diagnosis

2019 ◽  
Vol 8 (4) ◽  
pp. 119-127
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pulmonary Tuberculosis ◽  
Soft Computing ◽  
Disease Diagnosis ◽  
Support Vector ◽  
Diagnostic Model ◽  
Soft Computing Techniques ◽  
Tuberculosis Diagnosis ◽  
Artificial Neural

Recently, several interesting research studies have been reported on soft computing approaches. Soft computing approaches are solving several kinds of problems and provide alternative solutions. Different Soft computing techniques or approaches have been applied in medical care data for effective diagnosis prediction. Those approaches implemented on diseases diagnosing of pulmonary tuberculosis and obtaining better results in comparison to traditional approaches. This approach is an aggregation of methodologies that were combined various model and provide solutions to those problems that are difficult to handle in real-world situations. Researchers keep developing of an accurate and reliable intelligent decision-making method for the construction of pulmonary tuberculosis diagnosis system. The existing diagnostic testing system procedures are not only tedious, they also take a long time to analyze. Therefore, the diagnosis of tuberculosis still requires further improvements to new rapid and accurate diagnostic model and techniques that enable higher sensitivity and specificity to be achieved, thus promoting disease control and Prevention. State of the art makes approaches to soft computing more powerful, more reliable and more efficient. The importance of this review paper is to distinguish the different soft computing approaches used to support pulmonary tuberculosis disease diagnosis, identification, prediction and intelligent classification. In the field, researchers and medical practitioners look forward to using approaches to soft computing. Some of these are an artificial neural network, genetic algorithm, and support vector machine, fuzzy logic etc. latest methods in the diagnostic field uses artificial neural network. Some of the other benefits of Artificial neural network is an easy - to - optimize, resources and adoptable non - linear modeling of expansive data sets and predictive inference accuracy demonstrating that artificial neural network could serve as a valuable decision support tool in various fields, including medicine

scholarly journals Provide a Diagnostic Model Using a Combination of Two Neural Network Algorithms and a Genetic Algorithm

2021 ◽  
Vol 10 (1) ◽  
pp. 91
Author(s):  
Farshad Minaei ◽  
Hassan Dosti ◽  
Ebrahim Salimi Turk ◽  
Amin Golabpour
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Disease Diagnosis ◽  
Diagnostic Model ◽  
Data Set ◽  
Swarm Optimization ◽  
Artificial Neural

Introduction: Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.Material and Methods: In this paper, a clinical decision support system for the diagnosis of gestational diabetes is presented by combining artificial neural network and meta-heuristic algorithm. In this study, four meta-innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Then these four algorithms were compared with each other. The data set contains 768 records and 8 dependent variables. This data set has 200 missing records, so the number of study records was reduced to 568 records.Results: The data were divided into two sets of training and testing by 10-Fold method. Then, all four algorithms of neural-genetic network, ant-neural colony network, neural network-particle Swarm optimization and neural network-cuckoo search on the data The trainings were performed and then evaluated by the test set. And the accuracy of 95.02 was obtained. Also, the final output of the algorithm was examined with two similar tasks and it was shown that the proposed model worked better.Conclusion: In this study showed that the combination of two neural network and genetic algorithms can provide a suitable predictive model for disease diagnosis.

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