scholarly journals Predictions of Laryngeal Cancer Using Neural Network in Kerman Shafa Hospital

2018 ◽  
Vol 7 ◽  
pp. 4
Author(s):  
Sadegh NejatZadeh ◽  
Fatemeh Rahimi ◽  
Amid Khatibi Bardsiri ◽  
Elham Vahidian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Laryngeal Cancer ◽  
Human Error ◽  
Medical Science ◽  
Specificity And Sensitivity ◽  
The Neural Network ◽  
Functional Features ◽  
Artificial Neural

Introduction: One of the challenges facing medical science is the time and correct diagnosis of diseases. Particularly with regard to certain diseases such as the types of cancer, which are the leading causes of death worldwide, their early diagnosis has a significant impact on the control and treatment of this disease. The use of intelligent decision support systems with high precision can be a good way to reduce human error due to fatigue and lack of experience. Therefore, the present study tries to predict the disease by using data mining techniques and taking into account the variables that influence the prediction of laryngeal cancer. Material and methods: This study is an analytical study. The data from the 249 cases referred to Shafa Hospital in Kerman in 2017 have been obtained. This study is based on the Crisp methodology and in the MATLAB software environment. First, in order to understand the laryngeal cancer, a review of related studies was conducted and interviewed by specialist physicians. Then, according to expert opinion, 24 variables were identified as effective factors in predicting laryngeal cancer. After clearing and preparing data, an artificial neural network model was used to predict the risk of laryngeal cancer. In the following, another model of the combination of the genetic algorithm and the neural network was created. Using genetic algorithm, 9 functional features of prediction of laryngeal cancer were determined from among the 24 selected variables, and artificial neural network was used to predict the risk of laryngeal cancer. Finally, the criteria for accuracy, specificity, and sensitivity were used to evaluate the two models.Results: The genetic algorithm reduced the complexity of the model by reducing the number of features from 24 to 9, but improved the average precision from 80% to 84%. Also, the model made with the characteristics selected by the genetic algorithm, increased the specificity and accuracy criteria by 13% and 8%, respectively.Conclusion: Combining the genetic algorithm with the neural network, in addition to improving the accuracy of prediction of laryngeal cancer, accelerates the diagnosis process, especially at the data collection stage, by reducing the number of characteristics required. Therefore, using this model as a smart decision system is suggested.

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 Modeling and Optimizing a New Culture Medium for In Vitro Rooting of G×N15 Prunus Rootstock using Artificial Neural Network-Genetic Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammad Mehdi Arab ◽  
Abbas Yadollahi ◽  
Maliheh Eftekhari ◽  
Hamed Ahmadi ◽  
Mohammad Akbari ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Culture Medium ◽  
In Vitro Rooting ◽  
Artificial Neural

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

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