scholarly journals Neural Network Model for Heart Disease Diagnosis

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Disease Diagnosis ◽  
Batch Size ◽  
Diagnosis System ◽  
Simulation Results ◽  
Heart Disease Diagnosis

The heart disease diagnosis system is proposed inthis study. This kind of diagnosis systems enhance medical careand helps doctors. In this paper, heart disease dataset fromkaggle web site is used. Neural Network is examined andanalyzed for different structures as an optimizer, loss function,and batch size. The simulation results show that the proposedneural network model has 90,16% accuracy.

Heart Disease Diagnosis System Using Fuzzy Logic

2018 ◽  
Author(s):  
Hasan Kahtan ◽  
Kamal Z. Zamli ◽  
Wan Nor Ashikin Wan Ahmad Fatthi ◽  
Azma Abdullah ◽  
Mansoor Abdulleteef ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Diagnosis System ◽  
Heart Disease Diagnosis

scholarly journals Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis

2017 ◽  
Vol 7 (2) ◽  
pp. 1023 ◽  
Author(s):  
Wiharto Wiharto ◽  
Hari Kusnanto ◽  
Herianto Herianto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Multivariate Analysis ◽  
Heart Disease ◽  
System Performance ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Features Selection ◽  
Diagnosis System

<span lang="EN-US">Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system</span><span> for</span><span lang="EN-US"> diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value</span><span> for</span><span lang="EN-US"> accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%</span><span>, accuracy 86,30% </span><span lang="EN-US"> and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors,</span><span lang="EN-US">symptoms and exercise ECG. The conclusion that can be drawn</span><span> is</span><span lang="EN-US"> that the proposed diagnosis system capable of delivering performance in the </span><span>very good</span><span lang="EN-US"> category, with a number of attributes that are not a lot of checks and a relatively low cost</span><span>.</span>

An Efficient System for Heart Disease Prediction Using Hybrid OFBAT with Rule-Based Fuzzy Logic Model

2016 ◽  
Vol 26 (04) ◽  
pp. 1750061 ◽  
Author(s):  
G. Thippa Reddy ◽  
Neelu Khare
Keyword(s):  
Fuzzy Logic ◽  
Heart Disease ◽  
Fuzzy System ◽  
Disease Diagnosis ◽  
Fuzzy Logic System ◽  
Classification Model ◽  
Rule Based ◽  
Diagnosis System ◽  
Heart Disease Diagnosis ◽  
Logic System

The objective of the work is to predict heart disease using computing techniques like an oppositional firefly with BAT and rule-based fuzzy logic (RBFL). The system would help the doctors to automate heart disease diagnosis and to enhance the medical care. In this paper, a hybrid OFBAT-RBFL heart disease diagnosis system is designed. Here, at first, the relevant features are selected from the dataset using locality preserving projection (LPP) algorithm which helps the diagnosis system to develop a classification model using the fuzzy logic system. After that, the rules for the fuzzy system are created from the sample data. Among the entire rules, the important and relevant group of rules are selected using OFBAT algorithm. Here, the opposition based learning (OBL) is hybrid to the firefly with BAT algorithm to improve the performance of the FAT algorithm while optimizing the rules of the fuzzy logic system. Next, the fuzzy system is designed with the help of designed fuzzy rules and membership functions so that classification can be carried out within the fuzzy system designed. At last, the experimentation is performed by means of publicly available UCI datasets, i.e., Cleveland, Hungarian and Switzerland datasets. The experimentation result proves that the RBFL prediction algorithm outperformed the existing approach by attaining the accuracy of 78%.

scholarly journals Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model

2015 ◽  
Vol 113 (7) ◽  
pp. 2360-2375 ◽  
Author(s):  
Stephanie Westendorff ◽  
Shenbing Kuang ◽  
Bahareh Taghizadeh ◽  
Opher Donchin ◽  
Alexander Gail
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Visual Target ◽  
Hand Position ◽  
Prediction Errors ◽  
Visually Guided ◽  
Target Error ◽  
Adaptation Mechanisms ◽  
Simulation Results

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement (“jump”) consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation.

scholarly journals CAPTCHA Recognition Method Based on CNN with Focal Loss

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhong Wang ◽  
Peibei Shi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Recognition Method ◽  
High Adhesion ◽  
Recognition Ability ◽  
Different Types ◽  
Simple Neural Network

In order to distinguish between computers and humans, CAPTCHA is widely used in links such as website login and registration. The traditional CAPTCHA recognition method has poor recognition ability and robustness to different types of verification codes. For this reason, the paper proposes a CAPTCHA recognition method based on convolutional neural network with focal loss function. This method improves the traditional VGG network structure and introduces the focal loss function to generate a new CAPTCHA recognition model. First, we perform preprocessing such as grayscale, binarization, denoising, segmentation, and annotation and then use the Keras library to build a simple neural network model. In addition, we build a terminal end-to-end neural network model for recognition for complex CAPTCHA with high adhesion and more interference pixel. By testing the CNKI CAPTCHA, Zhengfang CAPTCHA, and randomly generated CAPTCHA, the experimental results show that the proposed method has a better recognition effect and robustness for three different datasets, and it has certain advantages compared with traditional deep learning methods. The recognition rate is 99%, 98.5%, and 97.84%, respectively.

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