The Convolution Neural Network Combined with the HT Person Fit Statistic to Develop an APP for Detecting Dengue Fever in Children: Development and Usability Study (Preprint)

2019 ◽  
Author(s):  
CHIEN WEI ◽  
Chi Chow Julie ◽  
Chou Willy
Keyword(s):  
Neural Network ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Family Members ◽  
Convolution Neural Network ◽  
Public Health Issue ◽  
Person Fit ◽  
Model Accuracy ◽  
Comparison Results

UNSTRUCTURED Backgrounds: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN), a well-established deep learning method, can improve prediction accuracy on account of its usage of a large number of parameters for modeling. Whether the HT person fit statistic can be combined with CNN to increase the prediction accuracy of the model and develop an application (APP) to detect DF in children remains unknown. Objectives: The aim of this study is to build a model for the automatic detection and classification of DF with symptoms to help patients, family members, and clinicians identify the disease at an early stage. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF) using CNN to predict DF risk. The accuracy of two sets of characteristics (19 symptoms and four other variables, including person mean, standard deviation, and two HT-related statistics matched to DF+ and DF−) for predicting DF, were then compared. Data were separated into training and testing sets, and the former was used to predict the latter. We calculated the sensitivity (Sens), specificity (Spec), and area under the receiver operating characteristic curve (AUC) across studies for comparison. Results: We observed that (1) the 23-item model yields a higher accuracy rate (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90) based on the 177-case training set; (2) the Sens values are almost higher than the corresponding Spec values (90% in 10 scenarios) for predicting DF; (3) the Sens and Spec values of the 23-item model are consistently higher than those of the 19-item model. An APP was subsequently designed to detect DF in children. Conclusion: The 23-item model yielded higher accuracy rates (0.95) and AUC (0.94) than the 19-item model (accuracy = 0.92, AUC = 0.90). An APP could be developed to help patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

scholarly journals Detecting Dengue Fever In Children Using A Combined Scheme Compared With Individual Algorithms: An APP Development And Usability Study

2021 ◽  
Author(s):  
Julie Chi Chow ◽  
Tsair-Wei Chien ◽  
Lin-Yen Wang ◽  
Willy Chou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dengue Fever ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Statistical Significance ◽  
Nearest Neighbors ◽  
Public Health Issue ◽  
K Nearest Neighbors ◽  
Artificial Neural

Abstract Background: Dengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN) and artificial neural networks(ANN) can improve prediction accuracy on account of its usage of a large number of parameters for modeling. A hypothesis using a combined scheme of algorithms, including convolutional neural networks(CNN), artificial neural networks(ANN), K-nearest Neighbors Algorithm(KNN), and logis-tical regression(LR), was made to improve the prediction DF accuracy for children. Methods: We extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF). A 11-variables were eligible by observing the statistical significance in predicting DF risk. The prediction accuracy was based on two training (80%) and testing (20%) sets on model accuracy of the area under the receiver operating characteristic curve (AUC) greater than 0.80 and 0.70, respectively, for discriminating DF+ and DF− in the two sets. Two scenarios of the combined scheme and individual algorithms were compared using the training set to predict the testing set. Results: We observed that (i) k-nearest neighbors algorithm has poorer AUC(<0.50), (ii)LR has relatively higher AUC(=0.70), and (ii) the three alternatives have almost equal AUC(=0.68), but smaller than the individual algorithms of NaiveBayes, Logistic regression in raw data and NaiveBayes in normalized data. Conclusion: An LR-based APP was designed to detect DF in children. The 11-item model is suggested to develop the APP for helping patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

scholarly journals Fruit Disease Detection Using Convolution Neural Network Approach

2018 ◽  
Vol 7 (2) ◽  
pp. 62-65
Author(s):  
Shivani . ◽  
Sharanjit Singh
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Texture Features ◽  
Convolution Neural Network ◽  
Image Feature ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Disease Information ◽  
Fruit Disease ◽  
Farming Industry

Fruit disease detection is critical at early stage since it will affect the farming industry. Farming industry is critical for the growth of the economic conditions of India. To this end, proposed system uses universal filter for the enhancement of image captured from source. This filter eliminates the noise if any from the image. This filter is not only tackle’s salt and pepper noise but also Gaussian noise from the image. Feature extraction operation is applied to extract colour and texture features. Segmented image so obtained is applied with Convolution neural network and k mean clustering for classification. CNN layers are applied to obtain optimised result in terms of classification accuracy. Clustering operation increases the speed with which classification operation is performed. The clusters contain the information about the disease information. Since clusters are formed so entire feature set is not required to be searched. Labelling information is compared against the appropriate clusters only. Results are improved by significant margin proving worth of the study.

scholarly journals Alcoholic brain injury via 8-layer customized deep convolution neural network

STEMedicine ◽  
2021 ◽  
Vol 2 (8) ◽  
pp. e97
Author(s):  
Ziquan Zhu ◽  
Mackenzie Brown
Keyword(s):  
Neural Network ◽  
Brain Injury ◽  
Diagnostic Errors ◽  
The Body ◽  
Convolution Neural Network ◽  
Comparison Results ◽  
Deep Convolution Neural Network ◽  
Sensitivity Specificity ◽  
And Behavior ◽  
Fully Connected

Alcohol can act quickly in the human body and alter mood and behavior. If alcohol is consumed in excess, it will accumulate in the organs of the body, especially in the liver and brain. To a certain extent, the symptoms of alcoholism will appear. So far, the main method of diagnosis of alcoholic brain injury is through MRI images by radiologists. However, this is a very subjective diagnosis. Radiologists may be affected by external factors, such as physical discomfort, lack of rest, inattention, etc., resulting in diagnostic errors. In this paper, we proposed a novel 8-layer customized deep convolution neural network for alcoholic brain injury detection, which contains five convolution layers, five pooling layers, and three fully connected layers. There are three improvements in this paper, (i) Based on deep learning, we proposed a method for automatic diagnosis of alcoholic brain injury; (ii) We introduced Dropout to the proposed structure to improve robustness; (iii) Compared with other state-of-the-art approaches, the proposed structure is more efficient. The experimental results showed that the sensitivity, specificity, precision, accuracy, F1, MCC and FMI were 96.14±1.99, 96.20±1.47, 95.98±1.54, 96.17±1.55, 96.05±1.62, 93.34±3.11, 96.06±1.62 respectively. According to comparison results, our method performed the best. The proposed model is effective in detecting alcoholic brain injury based on MRI images.

scholarly journals Detecting Dengue Fever in Children Using A Combined Scheme Compared with Individual Algorithms: An APP Development and Usability Study

2021 ◽  
Author(s):  
Julie Chi Chow ◽  
CHIEN TSAI WEI ◽  
Willy Chou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Early Stage ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Nearest Neighbors ◽  
Public Health Issue ◽  
K Nearest Neighbors ◽  
Artificial Neural

Abstract BackgroundDengue fever (DF) is an important public health issue in Asia. However, the disease is extremely hard to detect using traditional dichotomous (i.e., absent vs. present) evaluations of symptoms. Convolution neural network (CNN) and artificial neural networks(ANN) can improve prediction accuracy on account of its usage of a large number of parameters for modeling. A hypothesis using a combined scheme of algorithms, including convolutional neural networks(CNN), artificial neural networks(ANN), K-nearest Neighbors Algorithm(KNN), and logis-tical regression(LR), was made to improve the prediction DF accuracy for children. MethodsWe extracted 19 feature variables of DF-related symptoms from 177 pediatric patients (69 diagnosed with DF). A 11-variables were eligible by observing the statistical significance in predicting DF risk. The prediction accuracy was based on two training (80%) and testing (20%) sets on model accuracy of the area under the receiver operating characteristic curve (AUC) greater than 0.80 and 0.70, respectively, for discriminating DF+ and DF− in the two sets. Two scenarios of the combined scheme and individual algorithms were compared using the training set to predict the testing set. ResultsWe observed that (i) k-nearest neighbors algorithm has poorer AUC(<0.50), (ii)LR has relatively higher AUC(=0.70), and (ii) the three alternatives have almost equal AUC(=0.68), but smaller than the individual algorithms of NaiveBayes, Logistic regression in raw data and NaiveBayes in normalized data. ConclusionAn LR-based APP was designed to detect DF in children. The 11-item model is suggested to develop the APP for helping patients, family members, and clinicians discriminate DF from other febrile illnesses at an early stage.

scholarly journals Model Jaringan Syaraf Tiruan dalam Peramalan Kasus Positif Covid-19 di Indonesia

2020 ◽  
Vol 3 (2) ◽  
pp. 53
Author(s):  
Wirawan Setialaksana ◽  
Dwi Reski Anandari Sulaiman ◽  
Shabrina Syntha Dewi ◽  
Chairunnisa Ar Lamasitudju ◽  
Nini Rahayu Ashadi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Accuracy ◽  
Network Models ◽  
Model Accuracy ◽  
Neural Network Models ◽  
Accuracy Measurement ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Mitigation steps to control Covid-19 outbreak in Indonesia need to take. One of those step is forecasting the spread of the disease. This study compare two artificial neural network models in catching the pattern of Covid-19 positive total cases in Indonesia. Data Training used in this study is Indonesian total positive cases of Covid-19 from March 2 until May 26. The next 10 days data become data testing to show the model accuracy in predicting Covid-19 total cases. MLP shows a better prediction comparing to ELM.Three different prediction accuracy measurement is used – MAE, MAPE, and RMSE. All of them shows less value in MLP than in ELM.

scholarly journals Alcoholism via 6-layer customized deep convolution neural network

STEMedicine ◽  
2021 ◽  
Vol 2 (7) ◽  
pp. e93
Author(s):  
Ziquan Zhu
Keyword(s):  
Neural Network ◽  
Nervous System ◽  
Network Structure ◽  
Human Body ◽  
Convolution Neural Network ◽  
Brain Images ◽  
Comparison Results ◽  
Deep Convolution Neural Network ◽  
Neural Network Structure ◽  
Sensitivity Specificity

Background: Alcoholism is caused by excessive alcohol into the human body. Alcohol primarily damages the central nervous system of the human body and causes the nervous system function disorder and inhibition. Severe addiction can lead to respiratory circulation center inhibition, paralysis and even death. So far, the diagnosis of alcoholism is done by radiologist's manual CT examination. However, the diagnosis process is time-consuming, subjective and boring for doctors. External factors, such as extreme fatigue, lack of sleep and mental concentration, can easily affect the diagnosis process.Methods: In order to solve this problem, this paper proposed a new neural network based on computer vision, which used deep convolution neural network to diagnose alcoholism automatically. A total of 216 brain images were collected. In the 6-layer customized deep convolution neural network structure, there were four convolution layers and two fully connected layers, and each convolution layer was connected with a pooling layer.Results: The results showed that the accuracy, sensitivity, specificity, precision, F1, MCC and FMI were 95.96%±1.44%, 95.96%±1.66%, 95.95%±1.67%, 95.73%±1.72%, 95.84%±1.48%, 91.92%±2.87% and 95.84%±1.48% respectively.Conclusion: It can be concluded from comparison results that the proposed neural network structure is more effective than four state-of-the-art approaches. The proposed method has high accuracy and can be used as a diagnostic method for alcoholism.

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